{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b9c63ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tushare as ts\n",
    "import time\n",
    "import re\n",
    "import os\n",
    "from funcs import *\n",
    "from sqlalchemy import text, create_engine\n",
    "from tqdm.notebook import tqdm\n",
    "from datetime import datetime,timedelta\n",
    "# from const import *\n",
    "# from 数据库读写函数 import *\n",
    "# from 数据处理函数 import *\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import tushare as ts\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.rolling import RollingOLS\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "token = '1501ffe708345cffa38d9bbc0bd371e93b4b7412e7a8e1f811d3c442'\n",
    "ts.set_token(token)\n",
    "pro = ts.pro_api(token)\n",
    "\n",
    "def func_backtest(dff_tp,low_rate,high_rate,ddds,ttp,期货前缀s,字典,nn):\n",
    "    if ttp == 1:\n",
    "        做多的品种 = dff_tp.rank(1,pct=True) > high_rate\n",
    "        做空的品种 = dff_tp.rank(1,pct=True) < low_rate\n",
    "    elif ttp == -1:\n",
    "        做多的品种 = dff_tp.rank(1,pct=True) < low_rate\n",
    "        做空的品种 = dff_tp.rank(1,pct=True) > high_rate\n",
    "        \n",
    "    dd_rets = []\n",
    "    for i in range(len(dff_tp)-nn):\n",
    "        s1_date = dff_tp.index[i]\n",
    "        s_date = dff_tp.index[i+nn-1]\n",
    "        e_date = dff_tp.index[i+nn]\n",
    "        longs = list(做多的品种.loc[s1_date][做多的品种.loc[s1_date]].index)\n",
    "        shorts = list(做空的品种.loc[s1_date][做空的品种.loc[s1_date]].index)\n",
    "        \n",
    "        longs_ret = []\n",
    "        for 品种 in longs:\n",
    "            longs_ret.append(ddds[期货前缀s.index(字典.get(品种))][s_date:e_date]['主力代码收益率'])\n",
    "\n",
    "        shorts_ret = []\n",
    "        for 品种 in shorts:\n",
    "            shorts_ret.append(ddds[期货前缀s.index(字典.get(品种))][s_date:e_date]['主力代码收益率'])\n",
    "        \n",
    "        if len(longs_ret) != 0 and len(shorts_ret) != 0:\n",
    "            dd_long_ret = pd.concat(longs_ret,axis=1)*1\n",
    "            dd_long_ret.columns = longs\n",
    "            dd_short_ret = pd.concat(shorts_ret,axis=1)*-1\n",
    "            dd_short_ret.columns = shorts\n",
    "        #     dd_ret = pd.concat([dd_long_ret,dd_short_ret],axis=1).mean(1)\n",
    "            dd_ret = (dd_long_ret.mean(1) + dd_short_ret.mean(1))/2\n",
    "            dd_rets.append(dd_ret)\n",
    "    ddd = pd.concat(dd_rets).sort_index()\n",
    "    ddd = ddd[~ddd.index.duplicated()]\n",
    "    return ddd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e642817d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f8b83a6ac9db4c9d91010d77dd27fe71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "期货前缀ss = os.listdir('D:/tushare_database/dailydata')\n",
    "期货前缀ss = [x.split('.')[0] for x in 期货前缀ss]\n",
    "\n",
    "期货前缀ss2 = []\n",
    "for 期货前缀 in tqdm(期货前缀ss):\n",
    "    df = get_df(期货前缀)\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "#     if (df_close.notnull().sum(1) != 0).sum() > 250 and df_close.columns[-1] > '2301':\n",
    "    if (df_close.notnull().sum(1) != 0).sum() > 250:\n",
    "        期货前缀ss2.append(期货前缀)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6bacbd61",
   "metadata": {},
   "outputs": [],
   "source": [
    "交易所s = ['CFFEX','DCE','CZCE','SHFE','INE','GFEX']\n",
    "dds = []\n",
    "for 交易所 in 交易所s:\n",
    "    dd = pro.fut_basic(exchange=交易所, fut_type='1')\n",
    "    dds.append(dd)\n",
    "df_所有合约 = pd.concat(dds)\n",
    "df_所有合约['list_date'] = pd.to_datetime(df_所有合约['list_date'])\n",
    "df_所有合约['delist_date'] = pd.to_datetime(df_所有合约['delist_date'])\n",
    "df_所有合约 = df_所有合约[df_所有合约['delist_date']>'2010-01-01']\n",
    "df_所有合约 = df_所有合约.sort_values(by=['ts_code']).reset_index(drop=True)\n",
    "df_所有合约['name'] = df_所有合约['name'].apply(lambda x:re.sub(r'[^\\u4e00-\\u9fa5]', '', x))\n",
    "all_codes = list(df_所有合约['ts_code'])\n",
    "code_delist_date_dict = dict(zip(df_所有合约['ts_code'].apply(lambda x:x.split('.')[0]),\n",
    "                                 df_所有合约['delist_date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efef9689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74b12191f53446729442d5ca2674be6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddds = []\n",
    "for 期货前缀 in tqdm(期货前缀ss2):\n",
    "    df = get_df(期货前缀)\n",
    "    df_open = df.pivot(index='t', columns='code', values='open')\n",
    "    df_high = df.pivot(index='t', columns='code', values='high')\n",
    "    df_low = df.pivot(index='t', columns='code', values='low')\n",
    "    df_close = df.pivot(index='t', columns='code', values='close')\n",
    "    df_close_pct = df_close.apply(lambda x:x.dropna().pct_change(),axis=1)\n",
    "    df_money = df.pivot(index='t', columns='code', values='amount')\n",
    "    df_money_ratio = df_money.div(df_money.sum(1),axis=0)\n",
    "    df_oi = df.pivot(index='t', columns='code', values='oi')\n",
    "\n",
    "#     合约最后交易日 = list(map(lambda x: pd.to_datetime(f'20{x[:2]}-{x[2:4]}-15'), df_close.columns))\n",
    "    合约最后交易日 = list(map(lambda x:code_delist_date_dict.get(期货前缀+x) , df_close.columns))\n",
    "    交易日 = list(df_close.index)\n",
    "    pp = []\n",
    "    for i in range(len(交易日)):\n",
    "        p = []\n",
    "        for j in range(len(合约最后交易日)):\n",
    "            p.append((合约最后交易日[j] - 交易日[i]).days)\n",
    "        pp.append(p)\n",
    "\n",
    "    df_合约剩余日期 = pd.DataFrame(pp, index=df_close.index, columns=df_close.columns)\n",
    "\n",
    "    dd_amount = df.pivot(index='t',columns='code',values='amount')\n",
    "    当前主力代码 = pd.Series(dd_amount.columns[np.argsort(-dd_amount.values,axis=1)[:,0]],index=dd_amount.index).shift(1)\n",
    "    当前次主力代码 = pd.Series(dd_amount.columns[np.argsort(-dd_amount.values,axis=1)[:,1]],index=dd_amount.index).shift(1)\n",
    "    当前次次主力代码 = pd.Series(dd_amount.columns[np.argsort(-dd_amount.values,axis=1)[:,2]],index=dd_amount.index).shift(1)\n",
    "    ddd = pd.concat([\n",
    "        当前主力代码,\n",
    "        当前次主力代码,\n",
    "        df_close.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_close.apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_open.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_high.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_low.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_合约剩余日期.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_合约剩余日期.apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_合约剩余日期.apply(lambda x: x.get(当前次次主力代码.get(x.name)), axis=1),\n",
    "        df_money.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_money.apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_money_ratio.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_money_ratio.apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_close.pct_change().apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_close.pct_change().apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_close.pct_change().apply(lambda x: x.get(当前次次主力代码.get(x.name)), axis=1),\n",
    "        ((df_open - df_close.shift(1))/df_close.shift(-1)).apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        ((df_open - df_close.shift(1))/df_close.shift(-1)).apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1),\n",
    "        df_oi.sum(1),\n",
    "        df_oi.apply(lambda x: x.get(当前主力代码.get(x.name)), axis=1),\n",
    "        df_oi.apply(lambda x: x.get(当前次主力代码.get(x.name)), axis=1)\n",
    "    ], axis=1)\n",
    "    ddd.columns = ['主力代码', '次主力代码',\n",
    "                   '主力close', '次主力close',\n",
    "                   '主力open',\n",
    "                   '主力high', '主力low',\n",
    "                   '主力剩余日','次主力剩余日','次次主力剩余日',\n",
    "                   '主力交易额','次主力交易额',\n",
    "                   '主力交易额占比','次主力交易额占比',\n",
    "                   '主力代码收益率',\n",
    "                   '次主力代码收益率',\n",
    "                   '次次主力代码收益率',\n",
    "                   '开盘涨跌幅',\n",
    "                   '开盘次主力涨跌幅',\n",
    "                   '持仓量',\n",
    "                   '主力持仓',\n",
    "                   '次主力持仓']\n",
    "    ddd['交易额总额'] = df_money.sum(1)\n",
    "    ddd['展期率'] = 365*(ddd['次主力close'] - ddd['主力close'])/(ddd['次主力剩余日'] - ddd['主力剩余日'])/ddd['主力close']\n",
    "    ddd['主次持仓之和'] = ddd['主力持仓'] + ddd['次主力持仓']\n",
    "    ddd['流动性倒数'] = ddd['主力代码收益率'].abs()/ddd['主力交易额']\n",
    "    ddd['成交额加权收益率'] = (df_close.pct_change()*df_money_ratio).sum(1)\n",
    "    \n",
    "    ddd['近月收益率'] = (ddd['主力剩余日'] < ddd['次主力剩余日'])*ddd['主力代码收益率'] + \\\n",
    "    (ddd['主力剩余日'] >= ddd['次主力剩余日'])*ddd['次主力代码收益率']\n",
    "    ddd['远月收益率'] = (ddd['主力剩余日'] >= ddd['次主力剩余日'])*ddd['主力代码收益率'] + \\\n",
    "    (ddd['主力剩余日'] < ddd['次主力剩余日'])*ddd['次主力代码收益率']\n",
    "    \n",
    "    ddd['近月收益率2'] = (ddd['次主力剩余日'] < ddd['次次主力剩余日'])*ddd['次主力代码收益率'] + \\\n",
    "    (ddd['次主力剩余日'] >= ddd['次次主力剩余日'])*ddd['次次主力代码收益率']\n",
    "    ddd['远月收益率2'] = (ddd['次主力剩余日'] < ddd['次次主力剩余日'])*ddd['次主力代码收益率'] + \\\n",
    "    (ddd['次主力剩余日'] >- ddd['次次主力剩余日'])*ddd['次次主力代码收益率']\n",
    "    \n",
    "    ddd['近月收益率3'] = (ddd['主力剩余日'] < ddd['次次主力剩余日'])*ddd['主力代码收益率'] + \\\n",
    "    (ddd['主力剩余日'] >= ddd['次次主力剩余日'])*ddd['次次主力代码收益率']\n",
    "    ddd['远月收益率3'] = (ddd['主力剩余日'] >= ddd['次次主力剩余日'])*ddd['主力代码收益率'] + \\\n",
    "    (ddd['主力剩余日'] < ddd['次次主力剩余日'])*ddd['次次主力代码收益率']\n",
    "    \n",
    "    ddd['基差收益率'] = ddd['近月收益率'] - ddd['远月收益率']\n",
    "    ddd['基差收益率2'] = ddd['近月收益率2'] - ddd['远月收益率2']\n",
    "    ddd['基差收益率3'] = ddd['近月收益率3'] - ddd['远月收益率3']\n",
    "    ddd['curveRSI'] = (df_close_pct*(df_close_pct > 0)).sum(1) / df_close_pct.abs().sum(1)\n",
    "    ddd['平均合约间价格变化率'] = df_close.pct_change(axis=1).sum(1)/(df_close.notnull().sum(1) - 1)\n",
    "    ddds.append(ddd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfa75f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff_ret = pd.concat([x['主力代码收益率'] for x in ddds],axis=1)\n",
    "dff_ret.columns = 期货前缀ss2\n",
    "\n",
    "dff01 = pd.concat([x['主力交易额'] for x in ddds],axis=1)\n",
    "dff02 = pd.concat([x['次主力交易额'] for x in ddds],axis=1)\n",
    "dff03 = pd.concat([x['交易额总额'] for x in ddds],axis=1)\n",
    "dff1 = pd.concat([x['展期率'] for x in ddds],axis=1)\n",
    "dff2 = pd.concat([x['开盘涨跌幅'] for x in ddds],axis=1)\n",
    "dff22 = pd.concat([x['开盘次主力涨跌幅'] for x in ddds],axis=1)\n",
    "dff3 = pd.concat([x['主力代码收益率'] for x in ddds],axis=1)\n",
    "dff4 = pd.concat([x['主力持仓'] for x in ddds],axis=1)\n",
    "dff5 = pd.concat([x['基差收益率'] for x in ddds],axis=1)\n",
    "dff8 = pd.concat([x['curveRSI'] for x in ddds],axis=1)\n",
    "dff81 = pd.concat([x['平均合约间价格变化率'] for x in ddds],axis=1)\n",
    "dff52 = pd.concat([x['基差收益率2'] for x in ddds],axis=1)\n",
    "dff53 = pd.concat([x['基差收益率3'] for x in ddds],axis=1)\n",
    "dff32 = pd.concat([x['次主力代码收益率'] for x in ddds],axis=1)\n",
    "\n",
    "dff_open = pd.concat([x['主力open'] for x in ddds],axis=1)\n",
    "dff_high = pd.concat([x['主力high'] for x in ddds],axis=1)\n",
    "dff_low = pd.concat([x['主力low'] for x in ddds],axis=1)\n",
    "dff_close = pd.concat([x['主力close'] for x in ddds],axis=1)\n",
    "\n",
    "N = 120\n",
    "n1 = 0\n",
    "HH = dff_high.shift(n1).rolling(N).max()\n",
    "LL = dff_low.shift(n1).rolling(N).min()\n",
    "HC = dff_close.shift(n1).rolling(N).max()\n",
    "LC = dff_close.shift(n1).rolling(N).min()\n",
    "HH.columns = 期货前缀ss2\n",
    "LL.columns = 期货前缀ss2\n",
    "HC.columns = 期货前缀ss2\n",
    "LC.columns = 期货前缀ss2\n",
    "dff_close.columns = 期货前缀ss2\n",
    "ATR = np.maximum(HH - LC, HC - LL)\n",
    "# ATR = ATR/dff_close\n",
    "dff6 = ATR.copy()\n",
    "dff7 = dff_close.copy()\n",
    "\n",
    "dff_high.columns = 期货前缀ss2\n",
    "dff_low.columns = 期货前缀ss2\n",
    "dff01.columns = 期货前缀ss2\n",
    "dff02.columns = 期货前缀ss2\n",
    "dff03.columns = 期货前缀ss2\n",
    "dff1.columns = 期货前缀ss2\n",
    "dff2.columns = 期货前缀ss2\n",
    "dff22.columns = 期货前缀ss2\n",
    "dff3.columns = 期货前缀ss2\n",
    "dff32.columns = 期货前缀ss2\n",
    "dff4.columns = 期货前缀ss2\n",
    "dff5.columns = 期货前缀ss2\n",
    "dff52.columns = 期货前缀ss2\n",
    "dff53.columns = 期货前缀ss2\n",
    "dff8.columns = 期货前缀ss2\n",
    "dff81.columns = 期货前缀ss2\n",
    "\n",
    "dff00 = (dff01 - dff02)/dff01\n",
    "dff00.columns = 期货前缀ss2\n",
    "\n",
    "s_date = '2010-01-01'\n",
    "d_date = '2023-12-30'\n",
    "\n",
    "dff_ret = dff_ret[s_date:d_date]\n",
    "\n",
    "dff_high = dff_high[s_date:d_date]\n",
    "dff_low = dff_low[s_date:d_date]\n",
    "dff01 = dff01[s_date:d_date]\n",
    "dff02 = dff02[s_date:d_date]\n",
    "dff03 = dff03[s_date:d_date]\n",
    "dff00 = dff00[s_date:d_date]\n",
    "dff1 = dff1[s_date:d_date]\n",
    "dff2 = dff2[s_date:d_date]\n",
    "dff22 = dff22[s_date:d_date]\n",
    "dff3 = dff3[s_date:d_date]\n",
    "dff32 = dff32[s_date:d_date]\n",
    "dff4 = dff4[s_date:d_date]\n",
    "dff5 = dff5[s_date:d_date]\n",
    "dff52 = dff52[s_date:d_date]\n",
    "dff53 = dff53[s_date:d_date]\n",
    "dff6 = dff6[s_date:d_date]\n",
    "dff7 = dff7[s_date:d_date]\n",
    "dff8 = dff8[s_date:d_date]\n",
    "dff81 = dff81[s_date:d_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb2f1573",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39d32d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x177100b6e50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp0 = 'M'\n",
    "tp1 = 'Q-JAN'\n",
    "tp2 = 'Q-FEB'\n",
    "tp3 = 'Q-MAR'\n",
    "\n",
    "rr = dff3.resample(tp0).sum().shift(-1)\n",
    "xx = dff2.resample(tp3).sum().resample(tp0).last().fillna(method='ffill')\n",
    "# xx = 基差率01.rolling(120).mean().resample(tp0).last()\n",
    "xx = xx.rank(1,pct=True)\n",
    "xx = (xx>0.8)*-1 + (xx<0.2)*1\n",
    "ret = (xx*rr).sum(1)/(xx!=0).sum(1)\n",
    "ret.cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f4316ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4188460030976718"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.46*ret.mean()/ret.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f810886",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76178ca8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "f019cf9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acb82dc910>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sig = dff3.rolling(15).sum()/dff3.abs().rolling(15).sum()\n",
    "rett = dff3.resample('M').sum().shift(-1)\n",
    "sig = sig.resample('M').last().rank(1,pct=True)\n",
    "sig = (sig > 0.8)*1 + (sig < 0.2)*-1\n",
    "ret = (sig*rett).sum(1)/(sig != 0).sum(1)\n",
    "ret.cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8fac181c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.994726863165618"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*ret['2019-01-01':'2021-06-01'].mean()/ret['2019-01-01':'2021-06-01'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "64bb4e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acb82866d0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff3['SC'].cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "519abebd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0fdaa0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>AG</th>\n",
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       "      <th>ZC</th>\n",
       "      <th>ZN</th>\n",
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       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>0.004705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010926</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.010037</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010144</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.002641</td>\n",
       "      <td>0.004044</td>\n",
       "      <td>0.010291</td>\n",
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       "      <td>0.002807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.002218</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-0.000504</td>\n",
       "      <td>0.000745</td>\n",
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       "      <td>0.010030</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.021643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.035348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.008411</td>\n",
       "      <td>0.023775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.008990</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.004360</td>\n",
       "      <td>0.004829</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.004220</td>\n",
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       "      <td>0.004157</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.005018</td>\n",
       "      <td>-0.035545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.005241</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.012410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.020562</td>\n",
       "      <td>-0.005242</td>\n",
       "      <td>-0.002516</td>\n",
       "      <td>-0.028430</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>-0.015579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011095</td>\n",
       "      <td>-0.006143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>-0.006065</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>-0.014758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.023705</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-11</th>\n",
       "      <td>0.000610</td>\n",
       "      <td>-0.024285</td>\n",
       "      <td>-0.005949</td>\n",
       "      <td>-0.015314</td>\n",
       "      <td>-0.007576</td>\n",
       "      <td>-0.003842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007474</td>\n",
       "      <td>-0.003780</td>\n",
       "      <td>-0.004404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>-0.002671</td>\n",
       "      <td>-0.000514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.004101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-12</th>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.000511</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>-0.000479</td>\n",
       "      <td>-0.003605</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.003462</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>-0.006836</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.026339</td>\n",
       "      <td>0.006861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-13</th>\n",
       "      <td>-0.017742</td>\n",
       "      <td>-0.007156</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001873</td>\n",
       "      <td>-0.018264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007279</td>\n",
       "      <td>-0.007038</td>\n",
       "      <td>-0.005668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001208</td>\n",
       "      <td>-0.021314</td>\n",
       "      <td>-0.004940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.025892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.018824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-14</th>\n",
       "      <td>-0.002874</td>\n",
       "      <td>0.032092</td>\n",
       "      <td>0.017735</td>\n",
       "      <td>0.008977</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>-0.004823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007499</td>\n",
       "      <td>0.007088</td>\n",
       "      <td>-0.001629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>-0.013778</td>\n",
       "      <td>-0.003595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.003942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-15</th>\n",
       "      <td>0.005801</td>\n",
       "      <td>-0.000499</td>\n",
       "      <td>0.004826</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001554</td>\n",
       "      <td>0.012001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007939</td>\n",
       "      <td>0.004602</td>\n",
       "      <td>-0.003263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>-0.004957</td>\n",
       "      <td>0.015636</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.001055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3391 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        AG        AL        AP        AU         B   BB  \\\n",
       "t                                                                             \n",
       "2010-01-04  0.004705       NaN -0.010926       NaN -0.006262  0.010037  NaN   \n",
       "2010-01-05  0.002218       NaN  0.019767       NaN  0.029558 -0.000969  NaN   \n",
       "2010-01-06  0.021643       NaN  0.035348       NaN -0.008411  0.023775  NaN   \n",
       "2010-01-07 -0.026481       NaN -0.017896       NaN  0.005018 -0.035545  NaN   \n",
       "2010-01-08 -0.015579       NaN -0.018783       NaN -0.011095 -0.006143  NaN   \n",
       "...              ...       ...       ...       ...       ...       ...  ...   \n",
       "2023-12-11  0.000610 -0.024285 -0.005949 -0.015314 -0.007576 -0.003842  0.0   \n",
       "2023-12-12  0.008130  0.000511  0.002720 -0.000479 -0.003605  0.006125  0.0   \n",
       "2023-12-13 -0.017742 -0.007156 -0.005697  0.000000 -0.001873 -0.018264  0.0   \n",
       "2023-12-14 -0.002874  0.032092  0.017735  0.008977  0.015309 -0.004823  0.0   \n",
       "2023-12-15  0.005801 -0.000499  0.004826  0.000000 -0.001554  0.012001  0.0   \n",
       "\n",
       "                  BC        BU         C  ...        TS        UR         V  \\\n",
       "t                                         ...                                 \n",
       "2010-01-04       NaN       NaN  0.010144  ...       NaN       NaN -0.003914   \n",
       "2010-01-05       NaN       NaN -0.000529  ...       NaN       NaN  0.008513   \n",
       "2010-01-06       NaN       NaN  0.008990  ...       NaN       NaN -0.005844   \n",
       "2010-01-07       NaN       NaN -0.005241  ...       NaN       NaN -0.012410   \n",
       "2010-01-08       NaN       NaN  0.014226  ...       NaN       NaN -0.011905   \n",
       "...              ...       ...       ...  ...       ...       ...       ...   \n",
       "2023-12-11  0.007474 -0.003780 -0.004404  ...  0.000396 -0.002671 -0.000514   \n",
       "2023-12-12 -0.003462  0.001084 -0.006836  ...  0.000000  0.026339  0.006861   \n",
       "2023-12-13 -0.007279 -0.007038 -0.005668  ...  0.001208 -0.021314 -0.004940   \n",
       "2023-12-14  0.007499  0.007088 -0.001629  ...  0.000257 -0.013778 -0.003595   \n",
       "2023-12-15  0.007939  0.004602 -0.003263  ...  0.000257 -0.004957  0.015636   \n",
       "\n",
       "            WH        WR        WS        WT         Y  ZC        ZN  \n",
       "t                                                                     \n",
       "2010-01-04 NaN  0.009302  0.002641  0.004044  0.010291 NaN  0.002807  \n",
       "2010-01-05 NaN  0.004147  0.000000 -0.000504  0.000745 NaN  0.010030  \n",
       "2010-01-06 NaN  0.004360  0.004829  0.001008  0.004220 NaN  0.004157  \n",
       "2010-01-07 NaN -0.020562 -0.005242 -0.002516 -0.028430 NaN -0.000690  \n",
       "2010-01-08 NaN -0.006065  0.000439  0.000505 -0.014758 NaN -0.023705  \n",
       "...         ..       ...       ...       ...       ...  ..       ...  \n",
       "2023-12-11 NaN -0.003488       NaN       NaN -0.001483 NaN -0.004101  \n",
       "2023-12-12 NaN  0.000467       NaN       NaN  0.001980 NaN  0.011628  \n",
       "2023-12-13 NaN -0.025892       NaN       NaN -0.018824 NaN -0.010776  \n",
       "2023-12-14 NaN  0.000479       NaN       NaN -0.003942 NaN  0.008230  \n",
       "2023-12-15 NaN -0.001436       NaN       NaN -0.001055 NaN  0.002161  \n",
       "\n",
       "[3391 rows x 77 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = dff_high.columns[i]\n",
    "X = dff_low[name]\n",
    "X = sm.add_constant(X)\n",
    "y = dff_high[name]\n",
    "model1 = RollingOLS(endog=y,\n",
    "                   exog=X,\n",
    "                   window=30,\n",
    "                   expanding=True,\n",
    "                   min_nobs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d83e9c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "78694365",
   "metadata": {},
   "outputs": [],
   "source": [
    "tp = 'M'\n",
    "tp1 = 'Q-MAR'\n",
    "tp2 = 'Q-JAN'\n",
    "tp3 = 'M'\n",
    "tp4 = '2M'\n",
    "tp5 = 'Q-FEB'\n",
    "\n",
    "dff00_tp3 = dff00.rolling(250).mean().resample(tp).last().fillna(method='ffill')\n",
    "dff1_tp1 = dff1.resample(tp1).mean().resample(tp).last().fillna(method='ffill') # 展期因子1\n",
    "dff1_tp2 = dff1.resample(tp2).mean().resample(tp).last().fillna(method='ffill') # 展期因子2\n",
    "dff1_tp5 = dff1.resample(tp5).mean().resample(tp).last().fillna(method='ffill') # 展期因子3\n",
    "dff2_tp1 = dff2.resample(tp1).mean().resample(tp).last().fillna(method='ffill') # 开盘因子1\n",
    "dff2_tp2 = dff2.resample(tp2).mean().resample(tp).last().fillna(method='ffill') # 开盘因子2\n",
    "dff2_tp5 = dff2.resample(tp5).mean().resample(tp).last().fillna(method='ffill') # 开盘因子3\n",
    "dff22_tp1 = dff22.resample(tp1).mean().resample(tp).last().fillna(method='ffill') # 开盘因子12\n",
    "dff22_tp2 = dff22.resample(tp2).mean().resample(tp).last().fillna(method='ffill') # 开盘因子22\n",
    "dff22_tp5 = dff22.resample(tp5).mean().resample(tp).last().fillna(method='ffill') # 开盘因子32\n",
    "dff31_tp3 = dff3.resample(tp3).sum().rolling(18).mean() # 动量因子1\n",
    "dff32_tp3 = dff3.rolling(250).mean().resample(tp3).last() # 动量因子2\n",
    "dff33_tp3 = dff3.rolling(120).skew().resample(tp3).last() # 偏度因子\n",
    "dff34_tp3 = dff3.rolling(120).std().resample(tp3).last() # 波动率因子\n",
    "dff35_tp3 = (dff3.rolling(230).mean() - dff3.rolling(250).mean()).resample(tp3).last() # 动量因子3\n",
    "dff36_tp3 = (dff3.rolling(10).mean() - dff3.rolling(250).mean()).resample(tp3).last() # 反转因子\n",
    "dff37_tp3 = ((dff3*(dff3>0)).rolling(120).sum()/dff3.abs().rolling(120).sum()).resample(tp).last()\n",
    "dff4_tp3 = dff4.pct_change().rolling(30).mean().resample(tp3).last() # 持仓因子\n",
    "dff5_tp3 = dff5.rolling(120).mean().resample(tp3).last() # 基差因子\n",
    "# dff50_tp3 = dff5.resample('M').mean().diff().rolling(6).sum()\n",
    "\n",
    "dff50_tp3 = dff5.diff().rolling(20).mean().resample(tp3).last()\n",
    "# dff50_tp3 = dff5.diff().resample(tp3).sum()\n",
    "dff501_tp3 = dff1.diff().rolling(120).mean().resample(tp3).last()\n",
    "# dff501_tp3 = dff1.diff().resample('6M').sum().resample(tp).last().fillna(method='ffill')\n",
    "\n",
    "# dff501_tp3 = dff1.diff().rolling(20).mean().resample(tp3).last()\n",
    "\n",
    "# dff50_tp3 = dff1.resample(tp1).mean().diff().resample(tp).last().fillna(method='ffill')\n",
    "# dff50_tp3 = dff5.resample(tp3).mean().diff().rolling(12).sum()\n",
    "# dff50_tp3 = (dff5.rolling(30).mean() / dff5.rolling(30).mean().shift(30) - 1).rolling(30).mean().resample('M').last()\n",
    "# dff50_tp3 = dff5.rolling(120).sum().resample(tp3).last()\n",
    "dff52_tp3 = dff52.rolling(120).mean().resample(tp3).last() # 基差因子2\n",
    "dff53_tp3 = dff53.rolling(120).mean().resample(tp3).last() # 基差因子3\n",
    "dff6_tp3 = dff6.resample(tp3).last() # ATR因子\n",
    "dff7_tp3 = dff7.resample(tp3).last() # 价格因子\n",
    "dff8_tp3 = dff8.rolling(20).mean().resample(tp3).last() # \n",
    "dff81_tp3 = dff81.rolling(20).mean().resample(tp3).last() # \n",
    "# RSRS1_tp3 = RSRS1.resample(tp3).mean() # RSRS因子\n",
    "# RSRS2_tp3 = RSRS2.resample(tp3).mean()\n",
    "# RSRS3_tp3 = RSRS3.resample(tp3).mean()\n",
    "\n",
    "ret_tp3 = dff_ret.resample(tp3).sum().shift(-1)\n",
    "\n",
    "con1 = dff01.rolling(120).mean() > 500000\n",
    "con2 = dff01.rolling(120).mean().rank(1,pct=True) > 0.2\n",
    "con = con1 & con2\n",
    "\n",
    "# df_cat = get_cat()\n",
    "# df_cat = df_cat[df_cat['category']!='fin']\n",
    "# 期货前缀s = list(df_cat.index)\n",
    "# 期货前缀s = [x.upper() for x in 期货前缀s]\n",
    "# con0 = pd.DataFrame(np.zeros((len(dff01.index),len(dff01.columns))),index=dff01.index,columns=dff01.columns)\n",
    "# con0[期货前缀s] = 1\n",
    "\n",
    "con_tp3 = con.resample(tp3).last()\n",
    "# con0_tp3 = con0.resample(tp3).last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "86bde6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "dff00_tp3_2 = (dff00_tp3*con_tp3).replace(0,np.nan)\n",
    "dff1_tp1_2 = (dff1_tp1*con_tp3).replace(0,np.nan)\n",
    "dff1_tp2_2 = (dff1_tp2*con_tp3).replace(0,np.nan)\n",
    "dff1_tp5_2 = (dff1_tp5*con_tp3).replace(0,np.nan)\n",
    "dff2_tp1_2 = (dff2_tp1*con_tp3).replace(0,np.nan)\n",
    "dff2_tp2_2 = (dff2_tp2*con_tp3).replace(0,np.nan)\n",
    "dff2_tp5_2 = (dff2_tp5*con_tp3).replace(0,np.nan)\n",
    "dff22_tp1_2 = (dff22_tp1*con_tp3).replace(0,np.nan)\n",
    "dff22_tp2_2 = (dff22_tp2*con_tp3).replace(0,np.nan)\n",
    "dff22_tp5_2 = (dff22_tp5*con_tp3).replace(0,np.nan)\n",
    "dff31_tp3_2 = (dff31_tp3*con_tp3).replace(0,np.nan)\n",
    "dff32_tp3_2 = (dff32_tp3*con_tp3).replace(0,np.nan)\n",
    "dff33_tp3_2 = (dff33_tp3*con_tp3).replace(0,np.nan)\n",
    "dff34_tp3_2 = (dff34_tp3*con_tp3).replace(0,np.nan)\n",
    "dff35_tp3_2 = (dff35_tp3*con_tp3).replace(0,np.nan)\n",
    "dff36_tp3_2 = (dff36_tp3*con_tp3).replace(0,np.nan)\n",
    "dff37_tp3_2 = (dff37_tp3*con_tp3).replace(0,np.nan)\n",
    "dff4_tp3_2 = (dff4_tp3*con_tp3).replace(0,np.nan)\n",
    "dff5_tp3_2 = (dff5_tp3*con_tp3).replace(0,np.nan)\n",
    "dff50_tp3_2 = (dff50_tp3*con_tp3).replace(0,np.nan)\n",
    "dff501_tp3_2 = (dff501_tp3*con_tp3).replace(0,np.nan)\n",
    "dff52_tp3_2 = (dff52_tp3*con_tp3).replace(0,np.nan)\n",
    "dff53_tp3_2 = (dff53_tp3*con_tp3).replace(0,np.nan)\n",
    "dff6_tp3_2 = (dff6_tp3*con_tp3).replace(0,np.nan)\n",
    "dff7_tp3_2 = (dff7_tp3*con_tp3).replace(0,np.nan)\n",
    "dff8_tp3_2 = (dff8_tp3*con_tp3).replace(0,np.nan)\n",
    "dff81_tp3_2 = (dff81_tp3*con_tp3).replace(0,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "ca449b56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dd_qty_param = pd.read_csv('basicdata/qty_param.csv',index_col=0)\n",
    "原始字母字典 = dict(zip([x.upper() for x in list(dd_qty_param.index)],list(dd_qty_param.index)))\n",
    "原始字母字典2 = dict(zip(list(dd_qty_param.index),[x.upper() for x in list(dd_qty_param.index)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "17d9a83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pps = []\n",
    "for i in range(len(dff1_tp1_2.columns)):\n",
    "    x = dff1_tp1_2.columns[i]\n",
    "    if 原始字母字典.get(x):\n",
    "        pps.append(x)\n",
    "\n",
    "dff00_tp3_2 = dff00_tp3_2[pps]\n",
    "dff1_tp1_2 = dff1_tp1_2[pps]\n",
    "dff1_tp2_2 = dff1_tp2_2[pps]\n",
    "dff1_tp5_2 = dff1_tp5_2[pps]\n",
    "dff2_tp1_2 = dff2_tp1_2[pps]\n",
    "dff2_tp2_2 = dff2_tp2_2[pps]\n",
    "dff2_tp5_2 = dff2_tp5_2[pps]\n",
    "dff22_tp1_2 = dff22_tp1_2[pps]\n",
    "dff22_tp2_2 = dff22_tp2_2[pps]\n",
    "dff22_tp5_2 = dff22_tp5_2[pps]\n",
    "dff31_tp3_2 = dff31_tp3_2[pps]\n",
    "dff32_tp3_2 = dff32_tp3_2[pps]\n",
    "dff33_tp3_2 = dff33_tp3_2[pps]\n",
    "dff34_tp3_2 = dff34_tp3_2[pps]\n",
    "dff35_tp3_2 = dff35_tp3_2[pps]\n",
    "dff36_tp3_2 = dff36_tp3_2[pps]\n",
    "dff37_tp3_2 = dff37_tp3_2[pps]\n",
    "dff4_tp3_2 = dff4_tp3_2[pps]\n",
    "dff5_tp3_2 = dff5_tp3_2[pps]\n",
    "dff50_tp3_2 = dff50_tp3_2[pps]\n",
    "dff501_tp3_2 = dff501_tp3_2[pps]\n",
    "dff52_tp3_2 = dff52_tp3_2[pps]\n",
    "dff53_tp3_2 = dff53_tp3_2[pps]\n",
    "dff6_tp3_2 = dff6_tp3_2[pps]\n",
    "dff7_tp3_2 = dff7_tp3_2[pps]\n",
    "dff8_tp3_2 = dff8_tp3_2[pps]\n",
    "dff81_tp3_2 = dff81_tp3_2[pps]\n",
    "# RSRS1_tp3 = RSRS1_tp3[pps]\n",
    "# RSRS2_tp3 = RSRS2_tp3[pps]\n",
    "# RSRS3_tp3 = RSRS3_tp3[pps]\n",
    "ret_tp3 = ret_tp3[pps]\n",
    "\n",
    "dff00_tp3_2.columns = [原始字母字典.get(x) for x in dff00_tp3_2.columns]\n",
    "dff1_tp1_2.columns = [原始字母字典.get(x) for x in dff1_tp1_2.columns]\n",
    "dff1_tp2_2.columns = [原始字母字典.get(x) for x in dff1_tp2_2.columns]\n",
    "dff1_tp5_2.columns = [原始字母字典.get(x) for x in dff1_tp5_2.columns]\n",
    "dff2_tp1_2.columns = [原始字母字典.get(x) for x in dff2_tp1_2.columns]\n",
    "dff2_tp2_2.columns = [原始字母字典.get(x) for x in dff2_tp2_2.columns]\n",
    "dff2_tp5_2.columns = [原始字母字典.get(x) for x in dff2_tp5_2.columns]\n",
    "dff22_tp1_2.columns = [原始字母字典.get(x) for x in dff22_tp1_2.columns]\n",
    "dff22_tp2_2.columns = [原始字母字典.get(x) for x in dff22_tp2_2.columns]\n",
    "dff22_tp5_2.columns = [原始字母字典.get(x) for x in dff22_tp5_2.columns]\n",
    "dff31_tp3_2.columns = [原始字母字典.get(x) for x in dff31_tp3_2.columns]\n",
    "dff32_tp3_2.columns = [原始字母字典.get(x) for x in dff32_tp3_2.columns]\n",
    "dff33_tp3_2.columns = [原始字母字典.get(x) for x in dff33_tp3_2.columns]\n",
    "dff34_tp3_2.columns = [原始字母字典.get(x) for x in dff34_tp3_2.columns]\n",
    "dff35_tp3_2.columns = [原始字母字典.get(x) for x in dff35_tp3_2.columns]\n",
    "dff36_tp3_2.columns = [原始字母字典.get(x) for x in dff36_tp3_2.columns]\n",
    "dff37_tp3_2.columns = [原始字母字典.get(x) for x in dff37_tp3_2.columns]\n",
    "dff4_tp3_2.columns = [原始字母字典.get(x) for x in dff4_tp3_2.columns]\n",
    "dff5_tp3_2.columns = [原始字母字典.get(x) for x in dff5_tp3_2.columns]\n",
    "dff50_tp3_2.columns = [原始字母字典.get(x) for x in dff50_tp3_2.columns]\n",
    "dff501_tp3_2.columns = [原始字母字典.get(x) for x in dff501_tp3_2.columns]\n",
    "dff52_tp3_2.columns = [原始字母字典.get(x) for x in dff52_tp3_2.columns]\n",
    "dff53_tp3_2.columns = [原始字母字典.get(x) for x in dff53_tp3_2.columns]\n",
    "dff6_tp3_2.columns = [原始字母字典.get(x) for x in dff6_tp3_2.columns]\n",
    "dff7_tp3_2.columns = [原始字母字典.get(x) for x in dff7_tp3_2.columns]\n",
    "dff8_tp3_2.columns = [原始字母字典.get(x) for x in dff8_tp3_2.columns]\n",
    "dff81_tp3_2.columns = [原始字母字典.get(x) for x in dff81_tp3_2.columns]\n",
    "ret_tp3.columns = [原始字母字典.get(x) for x in ret_tp3.columns]\n",
    "# RSRS1_tp3.columns = [原始字母字典.get(x) for x in RSRS1_tp3.columns]\n",
    "# RSRS2_tp3.columns = [原始字母字典.get(x) for x in RSRS2_tp3.columns]\n",
    "# RSRS3_tp3.columns = [原始字母字典.get(x) for x in RSRS3_tp3.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "1bf72ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_rate = 0.2\n",
    "high_rate = 0.8\n",
    "\n",
    "ddd003_2 = func_backtest(dff00_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd11_2 = func_backtest(dff1_tp1_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd12_2 = func_backtest(dff1_tp2_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd15_2 = func_backtest(dff1_tp5_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd21_2 = func_backtest(dff2_tp1_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd22_2 = func_backtest(dff2_tp2_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd25_2 = func_backtest(dff2_tp5_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd221_2 = func_backtest(dff22_tp1_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd222_2 = func_backtest(dff22_tp2_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd225_2 = func_backtest(dff22_tp5_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd31_2 = func_backtest(dff31_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd32_2 = func_backtest(dff32_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd33_2 = func_backtest(dff33_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd34_2 = func_backtest(dff34_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd35_2 = func_backtest(dff35_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd36_2 = func_backtest(dff36_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd37_2 = func_backtest(dff37_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd4_2 = func_backtest(dff4_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd5_2 = func_backtest(dff5_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd50_2 = func_backtest(dff50_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd501_2 = func_backtest(dff501_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd52_2 = func_backtest(dff52_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd53_2 = func_backtest(dff53_tp3_2,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd6_2 = func_backtest(dff6_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd7_2 = func_backtest(dff7_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd8_2 = func_backtest(dff8_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "ddd81_2 = func_backtest(dff81_tp3_2,low_rate,high_rate,ddds,-1,期货前缀ss2,原始字母字典2,1)\n",
    "# ddd81_2 = func_backtest(RSRS1_tp3,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "# ddd82_2 = func_backtest(RSRS2_tp3,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)\n",
    "# ddd83_2 = func_backtest(RSRS3_tp3,low_rate,high_rate,ddds,1,期货前缀ss2,原始字母字典2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e8b78c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "57895b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1386367140650446,\n",
       " 0.9438679275552807,\n",
       " 0.834297972906646,\n",
       " 1.262330147267032,\n",
       " 1.1376567198546712,\n",
       " 1.184664485804317)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*ddd81_2.mean()/ddd81_2.std(),\\\n",
    "(250**0.5)*ddd8_2.mean()/ddd8_2.std(),(250**0.5)*ddd5_2.mean()/ddd5_2.std(),\\\n",
    "(250**0.5)*ddd11_2.mean()/ddd11_2.std(),(250**0.5)*ddd12_2.mean()/ddd12_2.std(),\\\n",
    "(250**0.5)*ddd15_2.mean()/ddd15_2.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1f319c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.051359586963344"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx = pd.concat([ddd8_2,ddd5_2,ddd11_2,ddd12_2,ddd15_2],axis=1).mean(1)\n",
    "xx = pd.concat([ddd8_2,ddd5_2],axis=1).mean(1)\n",
    "(250**0.5)*xx.mean()/xx.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f782b89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19a25d54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "757ff249",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838055</td>\n",
       "      <td>0.444848</td>\n",
       "      <td>0.752869</td>\n",
       "      <td>0.737509</td>\n",
       "      <td>0.755773</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.838055</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.711227</td>\n",
       "      <td>0.686722</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.752869</td>\n",
       "      <td>0.711227</td>\n",
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       "      <td>0.847108</td>\n",
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       "      <td>0.737509</td>\n",
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       "          0         1         2         3         4         5\n",
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       "1  0.838055  1.000000  0.430041  0.711227  0.686722  0.723950\n",
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      ]
     },
     "execution_count": 180,
     "metadata": {},
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    }
   ],
   "source": [
    "pd.concat([ddd81_2,ddd8_2,ddd5_2,ddd11_2,ddd12_2,ddd15_2],axis=1).resample('W').sum().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f2d3a4c",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12ac15df",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "174ef15c",
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aca4e491c0>"
      ]
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddd5_2.cumsum().plot()\n",
    "ddd50_2.cumsum().plot()\n",
    "ddd501_2.cumsum().plot(figsize=(14,6),grid=True)\n",
    "plt.legend(['基差因子','基差变化因子','展期变化因子'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f72186",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7216ae4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acac77d310>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddd50_2['2023-12-01':].cumsum().plot()\n",
    "ddd501_2['2023-12-01':].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1cfb16e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2010-08-02   -0.004509\n",
       "2010-08-03   -0.003207\n",
       "2010-08-04    0.000199\n",
       "2010-08-05   -0.004372\n",
       "2010-08-06   -0.003569\n",
       "                ...   \n",
       "2023-11-24   -0.004724\n",
       "2023-11-27    0.003113\n",
       "2023-11-28   -0.002278\n",
       "2023-11-29    0.007622\n",
       "2023-11-30   -0.000255\n",
       "Length: 3240, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd50_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "88af2b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\n",
    "    '主次差因子',\n",
    "    '展期因子-三月',\n",
    "    '展期因子-一月',\n",
    "    '展期因子-二月',\n",
    "    '开盘因子-三月','开盘因子-一月','开盘因子-二月',\n",
    "    '开盘因子2-三月','开盘因子2-一月','开盘因子2-二月',\n",
    "    '动量因子1','动量因子2','偏度因子',\n",
    "    '波动率因子','动量因子3','反转因子','RSI因子',\n",
    "    '持仓因子',\n",
    "    '基差因子','基差因子2','基差因子3',\n",
    "    'ATR因子','价格因子',\n",
    "    '基差变化因子','展期变化因子'\n",
    "]\n",
    "\n",
    "dddd2 = pd.concat([\n",
    "    ddd003_2,\n",
    "    ddd11_2,ddd12_2,ddd15_2,\n",
    "    ddd21_2,ddd22_2,ddd25_2,\n",
    "    ddd221_2,ddd222_2,ddd225_2,\n",
    "    ddd31_2,ddd32_2,ddd33_2,ddd34_2,ddd35_2,ddd36_2,ddd37_2,\n",
    "    ddd4_2,\n",
    "    ddd5_2,ddd52_2,ddd53_2,\n",
    "    ddd6_2,ddd7_2,\n",
    "    ddd50_2,ddd501_2\n",
    "    \n",
    "],axis=1)\n",
    "\n",
    "选择因子 = [\n",
    "#     '主次差因子',\n",
    "    '展期因子-三月',\n",
    "    '展期因子-一月',\n",
    "    '展期因子-二月',\n",
    "#     '行业展期因子-三月',\n",
    "#     '行业展期因子-一月',\n",
    "#     '行业展期因子-二月',\n",
    "#     '展期因子合并',\n",
    "#     '开盘因子合并',\n",
    "#     '开盘因子-三月合并',\n",
    "#     '开盘因子-一月合并',\n",
    "    '开盘因子-三月','开盘因子-一月',\n",
    "#     '开盘因子2-三月','开盘因子2-一月',\n",
    "    '动量因子1','动量因子2','偏度因子',\n",
    "    '动量因子3','反转因子',\n",
    "    '基差因子','基差因子3','ATR因子','价格因子',\n",
    "    '基差变化因子','展期变化因子'\n",
    "]\n",
    "\n",
    "dddd2.columns = names\n",
    "\n",
    "dddd2['展期因子合并'] = (dddd2['展期因子-三月'] + dddd2['展期因子-一月'] + dddd2['展期因子-二月'])/3\n",
    "dddd2['开盘因子-三月合并'] = (2/3)*dddd2['开盘因子-三月'] + (1/3)*dddd2['开盘因子2-三月']\n",
    "dddd2['开盘因子-一月合并'] = (2/3)*dddd2['开盘因子-一月'] + (1/3)*dddd2['开盘因子2-一月']\n",
    "dddd2['开盘因子合并'] = (1/2)*dddd2['开盘因子-三月合并'] + (1/2)*dddd2['开盘因子-一月合并']\n",
    "\n",
    "# ddd2 = pd.read_csv('行业的展期信号收益率.csv',index_col=0)\n",
    "# ddd2.index = pd.to_datetime(ddd2.index)\n",
    "\n",
    "# dddd2 = pd.concat([dddd2,ddd2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "814f825a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acb31cd8e0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2[选择因子].mean(1)['2023-12-01':].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9ccadb04",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>展期因子-三月</th>\n",
       "      <th>展期因子-一月</th>\n",
       "      <th>展期因子-二月</th>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <th>动量因子1</th>\n",
       "      <th>动量因子2</th>\n",
       "      <th>偏度因子</th>\n",
       "      <th>动量因子3</th>\n",
       "      <th>反转因子</th>\n",
       "      <th>基差因子</th>\n",
       "      <th>基差因子3</th>\n",
       "      <th>ATR因子</th>\n",
       "      <th>价格因子</th>\n",
       "      <th>基差变化因子</th>\n",
       "      <th>展期变化因子</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>展期因子-三月</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850830</td>\n",
       "      <td>0.831613</td>\n",
       "      <td>-0.025043</td>\n",
       "      <td>0.072234</td>\n",
       "      <td>0.529564</td>\n",
       "      <td>0.534874</td>\n",
       "      <td>-0.029917</td>\n",
       "      <td>0.058479</td>\n",
       "      <td>-0.040178</td>\n",
       "      <td>0.355490</td>\n",
       "      <td>0.318002</td>\n",
       "      <td>-0.020598</td>\n",
       "      <td>0.065195</td>\n",
       "      <td>0.049107</td>\n",
       "      <td>-0.110995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期因子-一月</th>\n",
       "      <td>0.850830</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825258</td>\n",
       "      <td>-0.017139</td>\n",
       "      <td>0.086616</td>\n",
       "      <td>0.544960</td>\n",
       "      <td>0.535739</td>\n",
       "      <td>-0.023783</td>\n",
       "      <td>0.059384</td>\n",
       "      <td>-0.010612</td>\n",
       "      <td>0.325486</td>\n",
       "      <td>0.343365</td>\n",
       "      <td>-0.048451</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.064063</td>\n",
       "      <td>-0.086247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期因子-二月</th>\n",
       "      <td>0.831613</td>\n",
       "      <td>0.825258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.045540</td>\n",
       "      <td>-0.002670</td>\n",
       "      <td>0.553277</td>\n",
       "      <td>0.557576</td>\n",
       "      <td>-0.015701</td>\n",
       "      <td>0.069550</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>0.331205</td>\n",
       "      <td>0.355664</td>\n",
       "      <td>-0.041027</td>\n",
       "      <td>0.025800</td>\n",
       "      <td>0.007114</td>\n",
       "      <td>-0.128666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <td>-0.025043</td>\n",
       "      <td>-0.017139</td>\n",
       "      <td>-0.045540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.486594</td>\n",
       "      <td>-0.009744</td>\n",
       "      <td>-0.043472</td>\n",
       "      <td>0.138816</td>\n",
       "      <td>0.034771</td>\n",
       "      <td>0.095444</td>\n",
       "      <td>-0.144743</td>\n",
       "      <td>-0.064385</td>\n",
       "      <td>0.056883</td>\n",
       "      <td>0.085633</td>\n",
       "      <td>0.033451</td>\n",
       "      <td>0.137687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <td>0.072234</td>\n",
       "      <td>0.086616</td>\n",
       "      <td>-0.002670</td>\n",
       "      <td>0.486594</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002658</td>\n",
       "      <td>-0.050121</td>\n",
       "      <td>0.138026</td>\n",
       "      <td>0.061624</td>\n",
       "      <td>0.016429</td>\n",
       "      <td>-0.012077</td>\n",
       "      <td>-0.048211</td>\n",
       "      <td>-0.001031</td>\n",
       "      <td>0.013388</td>\n",
       "      <td>0.070128</td>\n",
       "      <td>-0.005755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子1</th>\n",
       "      <td>0.529564</td>\n",
       "      <td>0.544960</td>\n",
       "      <td>0.553277</td>\n",
       "      <td>-0.009744</td>\n",
       "      <td>-0.002658</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.781494</td>\n",
       "      <td>0.014671</td>\n",
       "      <td>0.028159</td>\n",
       "      <td>-0.025370</td>\n",
       "      <td>0.184921</td>\n",
       "      <td>0.185262</td>\n",
       "      <td>-0.028973</td>\n",
       "      <td>0.038263</td>\n",
       "      <td>0.055653</td>\n",
       "      <td>-0.058250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子2</th>\n",
       "      <td>0.534874</td>\n",
       "      <td>0.535739</td>\n",
       "      <td>0.557576</td>\n",
       "      <td>-0.043472</td>\n",
       "      <td>-0.050121</td>\n",
       "      <td>0.781494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044483</td>\n",
       "      <td>0.046813</td>\n",
       "      <td>-0.112440</td>\n",
       "      <td>0.175854</td>\n",
       "      <td>0.271308</td>\n",
       "      <td>-0.022710</td>\n",
       "      <td>0.040755</td>\n",
       "      <td>0.051143</td>\n",
       "      <td>-0.084485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>偏度因子</th>\n",
       "      <td>-0.029917</td>\n",
       "      <td>-0.023783</td>\n",
       "      <td>-0.015701</td>\n",
       "      <td>0.138816</td>\n",
       "      <td>0.138026</td>\n",
       "      <td>0.014671</td>\n",
       "      <td>-0.044483</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010667</td>\n",
       "      <td>0.076631</td>\n",
       "      <td>-0.136234</td>\n",
       "      <td>-0.049878</td>\n",
       "      <td>-0.035801</td>\n",
       "      <td>0.032066</td>\n",
       "      <td>0.084429</td>\n",
       "      <td>0.010597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子3</th>\n",
       "      <td>0.058479</td>\n",
       "      <td>0.059384</td>\n",
       "      <td>0.069550</td>\n",
       "      <td>0.034771</td>\n",
       "      <td>0.061624</td>\n",
       "      <td>0.028159</td>\n",
       "      <td>0.046813</td>\n",
       "      <td>-0.010667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.118951</td>\n",
       "      <td>0.039980</td>\n",
       "      <td>0.046344</td>\n",
       "      <td>-0.046572</td>\n",
       "      <td>-0.023510</td>\n",
       "      <td>0.024218</td>\n",
       "      <td>-0.067359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>反转因子</th>\n",
       "      <td>-0.040178</td>\n",
       "      <td>-0.010612</td>\n",
       "      <td>0.009612</td>\n",
       "      <td>0.095444</td>\n",
       "      <td>0.016429</td>\n",
       "      <td>-0.025370</td>\n",
       "      <td>-0.112440</td>\n",
       "      <td>0.076631</td>\n",
       "      <td>-0.118951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004297</td>\n",
       "      <td>-0.064212</td>\n",
       "      <td>-0.086741</td>\n",
       "      <td>-0.075892</td>\n",
       "      <td>0.030902</td>\n",
       "      <td>0.022817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差因子</th>\n",
       "      <td>0.355490</td>\n",
       "      <td>0.325486</td>\n",
       "      <td>0.331205</td>\n",
       "      <td>-0.144743</td>\n",
       "      <td>-0.012077</td>\n",
       "      <td>0.184921</td>\n",
       "      <td>0.175854</td>\n",
       "      <td>-0.136234</td>\n",
       "      <td>0.039980</td>\n",
       "      <td>0.004297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.473721</td>\n",
       "      <td>-0.102010</td>\n",
       "      <td>-0.101783</td>\n",
       "      <td>-0.014653</td>\n",
       "      <td>-0.390714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差因子3</th>\n",
       "      <td>0.318002</td>\n",
       "      <td>0.343365</td>\n",
       "      <td>0.355664</td>\n",
       "      <td>-0.064385</td>\n",
       "      <td>-0.048211</td>\n",
       "      <td>0.185262</td>\n",
       "      <td>0.271308</td>\n",
       "      <td>-0.049878</td>\n",
       "      <td>0.046344</td>\n",
       "      <td>-0.064212</td>\n",
       "      <td>0.473721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073060</td>\n",
       "      <td>-0.077448</td>\n",
       "      <td>0.009432</td>\n",
       "      <td>-0.280376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATR因子</th>\n",
       "      <td>-0.020598</td>\n",
       "      <td>-0.048451</td>\n",
       "      <td>-0.041027</td>\n",
       "      <td>0.056883</td>\n",
       "      <td>-0.001031</td>\n",
       "      <td>-0.028973</td>\n",
       "      <td>-0.022710</td>\n",
       "      <td>-0.035801</td>\n",
       "      <td>-0.046572</td>\n",
       "      <td>-0.086741</td>\n",
       "      <td>-0.102010</td>\n",
       "      <td>-0.073060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.803503</td>\n",
       "      <td>-0.044699</td>\n",
       "      <td>0.053633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格因子</th>\n",
       "      <td>0.065195</td>\n",
       "      <td>0.020528</td>\n",
       "      <td>0.025800</td>\n",
       "      <td>0.085633</td>\n",
       "      <td>0.013388</td>\n",
       "      <td>0.038263</td>\n",
       "      <td>0.040755</td>\n",
       "      <td>0.032066</td>\n",
       "      <td>-0.023510</td>\n",
       "      <td>-0.075892</td>\n",
       "      <td>-0.101783</td>\n",
       "      <td>-0.077448</td>\n",
       "      <td>0.803503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>0.043430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差变化因子</th>\n",
       "      <td>0.049107</td>\n",
       "      <td>0.064063</td>\n",
       "      <td>0.007114</td>\n",
       "      <td>0.033451</td>\n",
       "      <td>0.070128</td>\n",
       "      <td>0.055653</td>\n",
       "      <td>0.051143</td>\n",
       "      <td>0.084429</td>\n",
       "      <td>0.024218</td>\n",
       "      <td>0.030902</td>\n",
       "      <td>-0.014653</td>\n",
       "      <td>0.009432</td>\n",
       "      <td>-0.044699</td>\n",
       "      <td>-0.003646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期变化因子</th>\n",
       "      <td>-0.110995</td>\n",
       "      <td>-0.086247</td>\n",
       "      <td>-0.128666</td>\n",
       "      <td>0.137687</td>\n",
       "      <td>-0.005755</td>\n",
       "      <td>-0.058250</td>\n",
       "      <td>-0.084485</td>\n",
       "      <td>0.010597</td>\n",
       "      <td>-0.067359</td>\n",
       "      <td>0.022817</td>\n",
       "      <td>-0.390714</td>\n",
       "      <td>-0.280376</td>\n",
       "      <td>0.053633</td>\n",
       "      <td>0.043430</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          展期因子-三月   展期因子-一月   展期因子-二月   开盘因子-三月   开盘因子-一月     动量因子1     动量因子2  \\\n",
       "展期因子-三月  1.000000  0.850830  0.831613 -0.025043  0.072234  0.529564  0.534874   \n",
       "展期因子-一月  0.850830  1.000000  0.825258 -0.017139  0.086616  0.544960  0.535739   \n",
       "展期因子-二月  0.831613  0.825258  1.000000 -0.045540 -0.002670  0.553277  0.557576   \n",
       "开盘因子-三月 -0.025043 -0.017139 -0.045540  1.000000  0.486594 -0.009744 -0.043472   \n",
       "开盘因子-一月  0.072234  0.086616 -0.002670  0.486594  1.000000 -0.002658 -0.050121   \n",
       "动量因子1    0.529564  0.544960  0.553277 -0.009744 -0.002658  1.000000  0.781494   \n",
       "动量因子2    0.534874  0.535739  0.557576 -0.043472 -0.050121  0.781494  1.000000   \n",
       "偏度因子    -0.029917 -0.023783 -0.015701  0.138816  0.138026  0.014671 -0.044483   \n",
       "动量因子3    0.058479  0.059384  0.069550  0.034771  0.061624  0.028159  0.046813   \n",
       "反转因子    -0.040178 -0.010612  0.009612  0.095444  0.016429 -0.025370 -0.112440   \n",
       "基差因子     0.355490  0.325486  0.331205 -0.144743 -0.012077  0.184921  0.175854   \n",
       "基差因子3    0.318002  0.343365  0.355664 -0.064385 -0.048211  0.185262  0.271308   \n",
       "ATR因子   -0.020598 -0.048451 -0.041027  0.056883 -0.001031 -0.028973 -0.022710   \n",
       "价格因子     0.065195  0.020528  0.025800  0.085633  0.013388  0.038263  0.040755   \n",
       "基差变化因子   0.049107  0.064063  0.007114  0.033451  0.070128  0.055653  0.051143   \n",
       "展期变化因子  -0.110995 -0.086247 -0.128666  0.137687 -0.005755 -0.058250 -0.084485   \n",
       "\n",
       "             偏度因子     动量因子3      反转因子      基差因子     基差因子3     ATR因子      价格因子  \\\n",
       "展期因子-三月 -0.029917  0.058479 -0.040178  0.355490  0.318002 -0.020598  0.065195   \n",
       "展期因子-一月 -0.023783  0.059384 -0.010612  0.325486  0.343365 -0.048451  0.020528   \n",
       "展期因子-二月 -0.015701  0.069550  0.009612  0.331205  0.355664 -0.041027  0.025800   \n",
       "开盘因子-三月  0.138816  0.034771  0.095444 -0.144743 -0.064385  0.056883  0.085633   \n",
       "开盘因子-一月  0.138026  0.061624  0.016429 -0.012077 -0.048211 -0.001031  0.013388   \n",
       "动量因子1    0.014671  0.028159 -0.025370  0.184921  0.185262 -0.028973  0.038263   \n",
       "动量因子2   -0.044483  0.046813 -0.112440  0.175854  0.271308 -0.022710  0.040755   \n",
       "偏度因子     1.000000 -0.010667  0.076631 -0.136234 -0.049878 -0.035801  0.032066   \n",
       "动量因子3   -0.010667  1.000000 -0.118951  0.039980  0.046344 -0.046572 -0.023510   \n",
       "反转因子     0.076631 -0.118951  1.000000  0.004297 -0.064212 -0.086741 -0.075892   \n",
       "基差因子    -0.136234  0.039980  0.004297  1.000000  0.473721 -0.102010 -0.101783   \n",
       "基差因子3   -0.049878  0.046344 -0.064212  0.473721  1.000000 -0.073060 -0.077448   \n",
       "ATR因子   -0.035801 -0.046572 -0.086741 -0.102010 -0.073060  1.000000  0.803503   \n",
       "价格因子     0.032066 -0.023510 -0.075892 -0.101783 -0.077448  0.803503  1.000000   \n",
       "基差变化因子   0.084429  0.024218  0.030902 -0.014653  0.009432 -0.044699 -0.003646   \n",
       "展期变化因子   0.010597 -0.067359  0.022817 -0.390714 -0.280376  0.053633  0.043430   \n",
       "\n",
       "           基差变化因子    展期变化因子  \n",
       "展期因子-三月  0.049107 -0.110995  \n",
       "展期因子-一月  0.064063 -0.086247  \n",
       "展期因子-二月  0.007114 -0.128666  \n",
       "开盘因子-三月  0.033451  0.137687  \n",
       "开盘因子-一月  0.070128 -0.005755  \n",
       "动量因子1    0.055653 -0.058250  \n",
       "动量因子2    0.051143 -0.084485  \n",
       "偏度因子     0.084429  0.010597  \n",
       "动量因子3    0.024218 -0.067359  \n",
       "反转因子     0.030902  0.022817  \n",
       "基差因子    -0.014653 -0.390714  \n",
       "基差因子3    0.009432 -0.280376  \n",
       "ATR因子   -0.044699  0.053633  \n",
       "价格因子    -0.003646  0.043430  \n",
       "基差变化因子   1.000000  0.025668  \n",
       "展期变化因子   0.025668  1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddd2[选择因子].resample('W').sum().corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "78b03bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2023-07-09     8\n",
       "2023-07-16    14\n",
       "2023-07-23    13\n",
       "2023-07-30     6\n",
       "2023-08-06    10\n",
       "2023-08-13    12\n",
       "2023-08-20    11\n",
       "2023-08-27     7\n",
       "2023-09-03    10\n",
       "2023-09-10     8\n",
       "2023-09-17     9\n",
       "2023-09-24     8\n",
       "2023-10-01     5\n",
       "2023-10-08     0\n",
       "2023-10-15     7\n",
       "2023-10-22    11\n",
       "2023-10-29     8\n",
       "2023-11-05    12\n",
       "2023-11-12    10\n",
       "2023-11-19     3\n",
       "2023-11-26    13\n",
       "2023-12-03    11\n",
       "2023-12-10     8\n",
       "2023-12-17     6\n",
       "Freq: W-SUN, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx = 100*dddd2[选择因子]['2023-07-01':].resample('W').sum().round(4)\n",
    "(xxx > 0).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e8b4241d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>展期因子-三月</th>\n",
       "      <th>展期因子-一月</th>\n",
       "      <th>展期因子-二月</th>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <th>动量因子1</th>\n",
       "      <th>动量因子2</th>\n",
       "      <th>偏度因子</th>\n",
       "      <th>动量因子3</th>\n",
       "      <th>反转因子</th>\n",
       "      <th>基差因子</th>\n",
       "      <th>基差因子3</th>\n",
       "      <th>ATR因子</th>\n",
       "      <th>价格因子</th>\n",
       "      <th>基差变化因子</th>\n",
       "      <th>展期变化因子</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-03</th>\n",
       "      <td>0.45</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.52</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-10</th>\n",
       "      <td>-1.92</td>\n",
       "      <td>-0.94</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>2.54</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>2.30</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>-1.44</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-0.13</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-17</th>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.98</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24</th>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>1.14</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>1.15</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.64</td>\n",
       "      <td>-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-15</th>\n",
       "      <td>-0.43</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-1.64</td>\n",
       "      <td>-1.68</td>\n",
       "      <td>-1.32</td>\n",
       "      <td>0.50</td>\n",
       "      <td>2.14</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-22</th>\n",
       "      <td>0.60</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.76</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-29</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.11</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.73</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-05</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.45</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-12</th>\n",
       "      <td>1.02</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.46</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.77</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-19</th>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.72</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>0.59</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-26</th>\n",
       "      <td>1.90</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.72</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.24</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-03</th>\n",
       "      <td>1.38</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.65</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.45</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-1.01</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.63</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-10</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.58</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-2.73</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.56</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>1.45</td>\n",
       "      <td>-0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-17</th>\n",
       "      <td>-0.96</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.93</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.68</td>\n",
       "      <td>-0.92</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            展期因子-三月  展期因子-一月  展期因子-二月  开盘因子-三月  开盘因子-一月  动量因子1  动量因子2  偏度因子  \\\n",
       "t                                                                             \n",
       "2023-09-03     0.45     0.21     0.13     0.52    -0.27  -0.25  -0.02  0.10   \n",
       "2023-09-10    -1.92    -0.94    -1.32     2.54     1.28   0.35  -0.99  2.30   \n",
       "2023-09-17     0.07    -0.04     0.12     1.35    -0.07   0.73   0.43  0.77   \n",
       "2023-09-24     0.06    -0.46    -0.69     0.13     0.35  -0.30   0.09  0.02   \n",
       "2023-10-01    -0.45    -0.05    -0.06     1.14    -0.22   1.15  -0.80 -0.20   \n",
       "2023-10-08     0.00     0.00     0.00     0.00     0.00   0.00   0.00  0.00   \n",
       "2023-10-15    -0.43    -0.19    -0.58     2.34     0.16  -1.64  -1.68 -1.32   \n",
       "2023-10-22     0.60     0.04     0.76    -1.46    -0.67   1.62   1.85  0.18   \n",
       "2023-10-29    -0.19    -0.09    -0.18     0.49     1.11  -0.53   0.01  0.17   \n",
       "2023-11-05     0.90     0.76     0.88    -1.12    -0.58   0.01  -0.45 -0.16   \n",
       "2023-11-12     1.02     0.27     0.92    -0.46    -1.86  -0.60   0.77 -1.07   \n",
       "2023-11-19    -0.47    -0.72    -0.36     0.59    -0.27  -0.81   0.01 -0.62   \n",
       "2023-11-26     1.90     1.92     1.72    -1.16    -1.26   1.12   0.81  0.53   \n",
       "2023-12-03     1.38     1.23     1.65    -0.02    -0.99   1.41   1.45 -0.53   \n",
       "2023-12-10     0.94     0.69     0.88    -0.00    -0.33   0.58  -0.12 -0.18   \n",
       "2023-12-17    -0.96    -0.63    -1.00     0.89     0.93  -0.93  -0.62  0.70   \n",
       "\n",
       "            动量因子3  反转因子  基差因子  基差因子3  ATR因子  价格因子  基差变化因子  展期变化因子  \n",
       "t                                                                  \n",
       "2023-09-03  -0.11 -1.07 -0.05   0.34  -0.70  0.31   -0.21    0.30  \n",
       "2023-09-10  -0.26  1.46 -1.44  -1.68   1.24 -0.13    0.04    0.44  \n",
       "2023-09-17  -0.08 -2.25 -0.42  -0.01   0.47  0.98   -0.93    0.35  \n",
       "2023-09-24  -0.25 -0.79 -0.15  -0.42   0.68  0.79   -0.91    0.83  \n",
       "2023-10-01   1.39  0.92 -0.18  -0.37  -0.15 -0.18    0.64   -0.08  \n",
       "2023-10-08   0.00  0.00  0.00   0.00   0.00  0.00    0.00    0.00  \n",
       "2023-10-15   0.50  2.14 -1.26  -0.70   0.32 -0.54    0.17    1.03  \n",
       "2023-10-22  -0.09 -0.10  1.77   0.81   1.26  1.45    0.89   -1.16  \n",
       "2023-10-29   0.51 -0.03 -1.01  -0.29   0.15  0.38   -0.73    0.63  \n",
       "2023-11-05   0.60  0.27  0.23   0.45   0.54  0.99    1.02    0.08  \n",
       "2023-11-12  -1.23 -0.50  0.13   0.75   0.16  1.53    0.91    1.28  \n",
       "2023-11-19  -0.44  0.07 -0.97  -0.62  -0.34 -0.04   -0.53   -0.21  \n",
       "2023-11-26   0.17 -0.51  1.02   1.44   1.48  2.06    1.24    0.61  \n",
       "2023-12-03  -0.86 -1.01  0.45   1.39   1.45  1.97    1.63    1.35  \n",
       "2023-12-10  -2.73  1.08  1.10   0.24  -0.56 -0.90    1.45   -0.66  \n",
       "2023-12-17   0.29  0.42  0.38  -0.17  -0.68 -0.92   -0.61   -0.02  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "844208ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acb33006a0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2[选择因子]['2023-07-01':].mean(1).cumsum().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2505cd4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "37d82ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>展期因子-三月</th>\n",
       "      <th>展期因子-一月</th>\n",
       "      <th>展期因子-二月</th>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <th>动量因子1</th>\n",
       "      <th>动量因子2</th>\n",
       "      <th>偏度因子</th>\n",
       "      <th>动量因子3</th>\n",
       "      <th>反转因子</th>\n",
       "      <th>基差因子</th>\n",
       "      <th>基差因子3</th>\n",
       "      <th>ATR因子</th>\n",
       "      <th>价格因子</th>\n",
       "      <th>基差变化因子</th>\n",
       "      <th>展期变化因子</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>展期因子-三月</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期因子-一月</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期因子-二月</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>偏度因子</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>动量因子3</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>反转因子</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差因子</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差因子3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATR因子</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格因子</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>基差变化因子</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>展期变化因子</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         展期因子-三月  展期因子-一月  展期因子-二月  开盘因子-三月  开盘因子-一月  动量因子1  动量因子2  偏度因子  \\\n",
       "展期因子-三月      1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "展期因子-一月      1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "展期因子-二月      1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "开盘因子-三月     -0.5     -0.5     -0.5      1.0      0.5   -0.5   -0.5   0.5   \n",
       "开盘因子-一月     -1.0     -1.0     -1.0      0.5      1.0   -1.0   -1.0   1.0   \n",
       "动量因子1        1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "动量因子2        1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "偏度因子        -1.0     -1.0     -1.0      0.5      1.0   -1.0   -1.0   1.0   \n",
       "动量因子3       -0.5     -0.5     -0.5      1.0      0.5   -0.5   -0.5   0.5   \n",
       "反转因子        -0.5     -0.5     -0.5     -0.5      0.5   -0.5   -0.5   0.5   \n",
       "基差因子        -0.5     -0.5     -0.5     -0.5      0.5   -0.5   -0.5   0.5   \n",
       "基差因子3        1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "ATR因子        1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "价格因子         1.0      1.0      1.0     -0.5     -1.0    1.0    1.0  -1.0   \n",
       "基差变化因子       0.5      0.5      0.5     -1.0     -0.5    0.5    0.5  -0.5   \n",
       "展期变化因子      -0.5     -0.5     -0.5      1.0      0.5   -0.5   -0.5   0.5   \n",
       "\n",
       "         动量因子3  反转因子  基差因子  基差因子3  ATR因子  价格因子  基差变化因子  展期变化因子  \n",
       "展期因子-三月   -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "展期因子-一月   -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "展期因子-二月   -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "开盘因子-三月    1.0  -0.5  -0.5   -0.5   -0.5  -0.5    -1.0     1.0  \n",
       "开盘因子-一月    0.5   0.5   0.5   -1.0   -1.0  -1.0    -0.5     0.5  \n",
       "动量因子1     -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "动量因子2     -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "偏度因子       0.5   0.5   0.5   -1.0   -1.0  -1.0    -0.5     0.5  \n",
       "动量因子3      1.0  -0.5  -0.5   -0.5   -0.5  -0.5    -1.0     1.0  \n",
       "反转因子      -0.5   1.0   1.0   -0.5   -0.5  -0.5     0.5    -0.5  \n",
       "基差因子      -0.5   1.0   1.0   -0.5   -0.5  -0.5     0.5    -0.5  \n",
       "基差因子3     -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "ATR因子     -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "价格因子      -0.5  -0.5  -0.5    1.0    1.0   1.0     0.5    -0.5  \n",
       "基差变化因子    -1.0   0.5   0.5    0.5    0.5   0.5     1.0    -1.0  \n",
       "展期变化因子     1.0  -0.5  -0.5   -0.5   -0.5  -0.5    -1.0     1.0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddd2[选择因子]['2023-12-01':].resample('W').sum().corr('spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d7a4971",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80096a99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "84e5cd2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\n",
    "    '主次差因子',\n",
    "    '展期因子-三月',\n",
    "    '展期因子-一月',\n",
    "    '展期因子-二月',\n",
    "    '开盘因子-三月','开盘因子-一月','开盘因子-二月',\n",
    "    '开盘因子2-三月','开盘因子2-一月','开盘因子2-二月',\n",
    "    '动量因子1','动量因子2','偏度因子',\n",
    "    '波动率因子','动量因子3','反转因子','RSI因子',\n",
    "    '持仓因子',\n",
    "    '基差因子','基差因子2','基差因子3',\n",
    "    'ATR因子','价格因子',\n",
    "    '基差变化因子','展期变化因子',\n",
    "    'curveRSI因子','平均合约间价格变化率因子'\n",
    "]\n",
    "\n",
    "dddd2 = pd.concat([\n",
    "    ddd003_2,\n",
    "    ddd11_2,ddd12_2,ddd15_2,\n",
    "    ddd21_2,ddd22_2,ddd25_2,\n",
    "    ddd221_2,ddd222_2,ddd225_2,\n",
    "    ddd31_2,ddd32_2,ddd33_2,ddd34_2,ddd35_2,ddd36_2,ddd37_2,\n",
    "    ddd4_2,\n",
    "    ddd5_2,ddd52_2,ddd53_2,\n",
    "    ddd6_2,ddd7_2,\n",
    "    ddd50_2,ddd501_2,\n",
    "    ddd8_2,ddd81_2\n",
    "    \n",
    "],axis=1)\n",
    "\n",
    "选择因子 = [\n",
    "#     '主次差因子',\n",
    "    '展期因子-三月',\n",
    "    '展期因子-一月',\n",
    "    '展期因子-二月',\n",
    "#     '行业展期因子-三月',\n",
    "#     '行业展期因子-一月',\n",
    "#     '行业展期因子-二月',\n",
    "#     '展期因子合并',\n",
    "#     '开盘因子合并',\n",
    "#     '开盘因子-三月合并',\n",
    "#     '开盘因子-一月合并',\n",
    "    '开盘因子-三月','开盘因子-一月',\n",
    "#     '开盘因子2-三月','开盘因子2-一月',\n",
    "    '动量因子1','动量因子2','偏度因子',\n",
    "    '动量因子3','反转因子',\n",
    "    '基差因子','基差因子3','ATR因子','价格因子',\n",
    "#     '基差变化因子','展期变化因子'\n",
    "    'curveRSI因子',\n",
    "    '平均合约间价格变化率因子'\n",
    "]\n",
    "\n",
    "dddd2.columns = names\n",
    "\n",
    "dddd2['展期因子合并'] = (dddd2['展期因子-三月'] + dddd2['展期因子-一月'] + dddd2['展期因子-二月'])/3\n",
    "dddd2['开盘因子-三月合并'] = (2/3)*dddd2['开盘因子-三月'] + (1/3)*dddd2['开盘因子2-三月']\n",
    "dddd2['开盘因子-一月合并'] = (2/3)*dddd2['开盘因子-一月'] + (1/3)*dddd2['开盘因子2-一月']\n",
    "dddd2['开盘因子合并'] = (1/2)*dddd2['开盘因子-三月合并'] + (1/2)*dddd2['开盘因子-一月合并']\n",
    "\n",
    "# ddd2 = pd.read_csv('行业的展期信号收益率.csv',index_col=0)\n",
    "# ddd2.index = pd.to_datetime(ddd2.index)\n",
    "\n",
    "# dddd2 = pd.concat([dddd2,ddd2],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "9ee12473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecd3e84145fb4b9d90dbb9a73ae32625",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/143 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.optimize as sco\n",
    "the_dates = list(ret_tp3['2012-01-01':].index)\n",
    "\n",
    "xxs2 = []\n",
    "xxs3 = []\n",
    "optxs2 = []\n",
    "optxs3 = []\n",
    "for i in tqdm(range(len(the_dates)-1)):\n",
    "# for i in tqdm(range(10)):\n",
    "    the_date1 = the_dates[i]\n",
    "    the_date2 = the_dates[i+1]\n",
    "    \n",
    "    dss21 = dddd2[选择因子].copy()\n",
    "#     dss21 = dss21['2012-01-01':the_date1].dropna()\n",
    "    dss21 = dss21[the_date1 - timedelta(days=365*2):the_date1].dropna()\n",
    "    dss22 = dddd2[选择因子].copy()\n",
    "    dss22 = dss22[the_date1 + timedelta(days=1):the_date2].dropna()\n",
    "    cov2 = dss21.cov()\n",
    "    \"\"\"\n",
    "    \"\"\"\n",
    "    def statistics(weights):\n",
    "        weights = np.array(weights)\n",
    "        port_returns = np.sum(dss21.mean()*weights)*252\n",
    "        port_variance = np.sqrt(np.dot(weights.T, np.dot(dss21.cov()*252,weights)))\n",
    "        return np.array([port_returns, port_variance, port_returns/port_variance])\n",
    "\n",
    "    def min_sharpe(weights):\n",
    "        return -statistics(weights)[2]\n",
    "\n",
    "    noa = len(dss21.columns)\n",
    "    cons = ({'type':'eq', 'fun':lambda x: np.sum(x)-1})\n",
    "    bnds = tuple((0,1) for x in range(noa))\n",
    "    opts2 = sco.minimize(min_sharpe,\n",
    "                         noa*[1./noa,], \n",
    "                         method = 'SLSQP',\n",
    "                         bounds = bnds, \n",
    "                         constraints = cons)\n",
    "    xx2 = (dss22*opts2.x).sum(1)\n",
    "    optxs2.append(list(opts2.x))\n",
    "    xxs2.append(xx2)\n",
    "    \n",
    "    def risk_budget_objective(weights,cov2):\n",
    "        weights = np.array(weights) #weights为一维数组\n",
    "        sigma = np.sqrt(np.dot(weights, np.dot(cov2, weights))) #获取组合标准差   \n",
    "        #sigma = np.sqrt(weights@cov@weights)\n",
    "        MRC = np.dot(cov2,weights)/sigma  #MRC = cov@weights/sigma\n",
    "        #MRC = np.dot(weights,cov)/sigma\n",
    "        TRC = weights * MRC\n",
    "        delta_TRC = [sum((i - TRC)**2) for i in TRC]\n",
    "        return sum(delta_TRC)\n",
    "\n",
    "    def total_weight_constraint(x):\n",
    "        return np.sum(x)-1.0\n",
    "\n",
    "    x0 = np.ones(cov2.shape[0]) / cov2.shape[0]\n",
    "    bnds = tuple((0,None) for x in x0)\n",
    "    cons = ({'type': 'eq', 'fun': total_weight_constraint})\n",
    "    #cons = ({'type':'eq', 'fun': lambda x: sum(x) - 1})\n",
    "    options={'disp':False, 'maxiter':1000, 'ftol':1e-20} \n",
    "\n",
    "    opts3 = sco.minimize(risk_budget_objective,\n",
    "                         x0,args=(cov2),\n",
    "                         bounds=bnds,\n",
    "                         constraints=cons,\n",
    "                         method='SLSQP',\n",
    "                         options=options)\n",
    "    xx3 = (dss22*opts3.x).sum(1)\n",
    "    optxs3.append(list(opts3.x))\n",
    "    xxs3.append(xx3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "b0f7e12a",
   "metadata": {},
   "outputs": [],
   "source": [
    "权重的动态变化 = pd.DataFrame(optxs2,index=the_dates[:-1],columns=选择因子).round(2)\n",
    "权重的动态变化 = 权重的动态变化[::-1]\n",
    "权重的动态变化3 = pd.DataFrame(optxs3,index=the_dates[:-1],columns=选择因子).round(2)\n",
    "权重的动态变化3 = 权重的动态变化3[::-1]\n",
    "dddd2['滚动找最优'] = pd.concat(xxs2)\n",
    "dddd2['滚动找RP'] = pd.concat(xxs3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "b80e2f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acac1b5d90>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3DMbDsvD2tCgpr2R24j6e/+k3nv/pN5KfmoSPlwd7c4vZnV1ERl4JcTFhtG7uV6NHxBhDYVklQb5n/se/3KIyVqZk4ecJ/75yINe9vYrz+7Ricl/t3SJnh0KMiIiISAPxztKd/GP2FvvnvJJy3rlxEB+tSOPLtXu474sNfJu4z379kteWcmG/1kyv7qU52vd/HMHkV5bw8hX9ySks4/Hq+7Zq7sfyB8addh2NMfR/Yh4APcM9CA30YfbdI077fiKnQyFGRERExMVsNsPTc7Yyc8lOAC7u35oVKVmc36cVA6NDWbWzaujW4QDj7WlRXmnYvC+Pzfvyar3n5FeWAPCnWesdzu8/VGJfUex0ZBWW2Y9bBmhmgriGQoyIiIjIGZq/JYOuUcFs2JtLiL8PI7q0qPN39+QUMeKfC+yf5/1lFF2igikpr8TXqyok3DKiA0Vllbzy8zYGRofw+jWx5BWXM/6lRQCM7NKC/YdK+PdVA/jr54ls2lt7sDls+4EC+rYNOY03hTW7cgA4t0cUU9rkn9Y9RM6UQoyIiIhIHZWUV+JhWfh4ebArq5BLX1vG7fGdeOr7rQ7lfn3oXCKCfet0z6MDTOJjE2ju7w2An7en/byXpwf3jO/KrSM74OPlga+XJ1HN/EidPrnG/b67eySXvLaUdWm5zPnjSL5N3Menv6bx3k2D2ZtTzO0freX+Lzfy/R9HnNJKYhl5JXz2627+b14yAK9eOYCVyxbX+fsizqQQIyIiIlIH2YVlDHyyai5IyjPn8/aSnWQVltUIMAA3v/crM68fhJ+3B8F+3se956e/ptmP598z2h5gjudE9zraJ7eeQ15JOZHBfvRs3Yz7J3UHoE+b5nRsEciW/Xl0eGAOs+8aQe82zai0GTw9LHZlFbF420GuHRpT455DnvnZftwpIhB/H88aZUTOFoUYERERkZNYmZLF1Bkr7J87PjinRpkbhsXQp01z7v08kQ17DjHquQXYjGHj4xOpsNnw8fTAy9NxDsnfv9wIwFvXxdE5Mshp9fXz9nToyTnMsixeuXIAF7xaNV9m0baDzFySwtfr9zmUW7I9k39NHWAPKruzi+zX/ji2M3eM6ey0uoqcDs3GEhERETmJw0OojvXVHcPoFBHIezcN5vGLejElti2Xx7YFoLi8ktIKG6t2ZnPBK0sY9+JClu/IAqCorMLhPj1bN6vfFzhK7zbNmX/PKIL9vHj+p99qBBiAnzZn0OPRH0k/VMKurEKueqsqwC1/YCz3TOhWa0ASOZvUEyMiIiJyAiXllaxPy2XaqI48eH4PsgpKiX8+gU6RQQyIDuXne+MdyndrGezw+ZqZKwHw9LC457P13Dg8hmfmJPH+TYMB+OO4LrQO8T8r73JY58hgpgxsy7vLUvGwwGZqL3fBq4vJLKhajczH04NWzc9uPUWORyFGRERE5ATeWLiDskobQztW7UwfHuTLrw+fiznOH/xvHtGBkAAfOrQI5N1lqcyuXhY5vmsEPycd4JWftwNw3durAJjcxzUbRD5yQU8KSiv4w+hOnPviQvt5H08PyiptAPYAA/D36nk1Ig2BQoyIiIjICXybuI9uUcGM6hphP3ei4VSWZXFZ9ZCy0vJKZifuo1NEIBf1b83PSQcoKHUcShbTIqB+Kn4Snh4WL1zeD4DF941h0baDnNMxnOKySjbtPcT9/9tIp4hAdhwsBODCfq4JWyK1UYgREREROQ5jDPtyi7n2nPZ4etR9OeLD4mLCuGFYDFcPiaZTRFCNjSdfvqI/vl6un1/SLiyAq4e0t3/u3aY5y3Zk2TfXvG5oeyKD/VxVPZEaFGJEREREqqVlFZFbXGbfCPJgfikl5bbTngvi4+XB4xf1qvXa3WM7c3H/Nqdd1/rWPvxID9HEXi1dWBORmhRiRERERKo98d0W5m/NYGpcO3q0CqawrBKAYZ3DnXL/b+4czpdr9/DApB4Nfp+V8T2jePWX7fRp05xhnZzz/iLOohAjIiIiQtUE/oXJBwj29eLT1bvt5/u2bU73ls5ZArlfuxD6tQtxyr3qW9+2ISQ9eZ6WU5YGSSFGREREmrT+T8zFZjPklVRNuL+4f0v+NK4LI59bAMAlAxrukK/6pgAjDZU2uxQREZEma3d2EblF5fYAA1WT2NuFBfDg+d2Jax/K1EHtXFhDEamNemJERESkycouLHP4/O6Ng+yT+qeN6sS0UZ1cUS0ROQn1xIiIiIjbM8YwY9EOEnfn8uwPW/luQ9XSwX/7ItFe5vnL+hLfLdJVVRSRU6CeGBEREXF7adlFPDMnyeFcXnEFyRkFAKx6aJz2QRFpRNQTIyIiIm5vXVpujXMPfrURgEcv6KkAI9LIKMSIiIiI21ublkOgjyfPTenLqgfHOVxryquPiTRWCjEiIiLi9n5Lz6d7q2b8flA7Ipv58eXtw+gaFcSmf0wkNNDH1dUTkVOkOTEiIiLitnZnF9n3e7kstq39fGz7UOb+ZbSrqiUiZ6hOPTGWZc20LGu5ZVkPH+e6l2VZaZZlJVT/08e51RQRERE5NcYYJry0yP754v6tXVgbEXGmk4YYy7IuBTyNMUOBjpZldamlWF/gE2NMfPU/G51dUREREZFTsSuriOLySjpHBvHGNQMZ0bmFq6skIk5Sl56YeOCz6uO5wIhaypwDXGBZ1qrqXhsNUxMRERGXuuez9QA8MKk75/VuhWVZLq6RiDiLZYw5cQHLmgm8YoxJtCxrAjDQGDP9mDKDgD3GmP2WZb0PfGGM+faYMtOAaQBRUVGxs2bNcuZ71FlBQQFBQUEuebbUL7Wte1P7uje1r/s6m227NauSNzeUcl6MN2OivbhtXhEAM8YH4OOpAFMf9LPr3lzdvmPGjFljjImr7VpdekwKAP/q4yBq773ZYIwprT5eDdQYcmaMmQHMAIiLizPx8fF1eLTzJSQk4KpnS/1S27o3ta97U/u6r7PVtouSD/LPH1cBMOu3MrxDWwG7uGd8VyaMq20kvDiDfnbdW0Nu37oMJ1vDkSFk/YDUWsp8YFlWP8uyPIHfAYnOqZ6IiIjIiRljuPfzRLw8LJ68uBcAH6zYBcCVg6NdWTURqSd1CTFfA9dalvUi8Htgs2VZTx1T5gngA2A9sNwYM9+51RQRERGp3YcrdnEwv5RHLujJtUNjuGl4B/u1iGBfF9ZMROrLSYeTGWPyLMuKB8YDzxlj0jmmp8UYs4mqFcpEREREzpofN+3nkW82A3DF4HYAXDUkmreX7mRy31aurJqI1KM6rSJmjMnhyAplIiIiIi5XWlHJfV9sAKBrVBC+Xp4AdI4MIunJ8zjJ2kUi0ohpKWQRERFpdIwxLPztIHklFfz53C7cMrKjw3U/b08X1UxEzoa6zIkRERERaVA+WbWbaR+sAWBir5YE+ervZUWaEoUYERERaXTeXroTgN5tmtG9ZbCLayMiZ5tCjIiIiDQqqZmF7MwspEtkEN/eOQLL0kaWIk2NQoyIiIg0KnO3pFNpM7x302A8PBRgRJoiDSAVERGRBs0YwztLUxkQHcLOzEKemZNEsK8XrUP8XV01EXERhRgRERFp0FIyC3niuy0O56aN6nic0iLSFCjEiIiISIO2LaPAfhzbPpTrh8VwUb/WLqyRiLiaQoyIiIg0WBWVNu74qGop5f+7vB9TYtu6uEYi0hBoYr+IiIg0KOWVNuZtyeBgfim7c4qxGbh0YBsFGBGxU0+MiIiINCifrd7NQ19tYmSXFmQWlAFwQd9WLq6ViDQkCjEiIiLSINhsBsuCpdszAVi8rep/e7dpxqguEa6smog0MAoxIiIi0iDc9N6vJPx2EIDIYF8O5JcC8PDknnh5agS8iByhECMiIiIul1NYZg8wAHExodw8oiMrUrIY0iHMhTUTkYZIIUZERERc5t2lO3ny+608MKm7w/kdBwqJbR9KbPtQF9VMRBoy9c2KiIg0IXtyiigqq2BlShb5JeWurg6Pz95Cpc3w1PdbAVj98Ln0adOcv4zv4uKaiUhDpp4YERGRJuKz1bu574sN9s/RYQEs/Fs8lmW5pD4Hq+e8HBYR7EuLIF9m3z3CJfURkcZDPTEiIiJNwM7MQocAA5CWXUReccVZr8vu7CI27jnE1W+tAODB86uGkn10y5CzXhcRaZzUEyMiItIEJO7OBWD6pX0Y2yOSX7Ye4P7/bWRnViH9A0Kc8ozd2UU88d0WRnRuwfXDYmotk1NYxsjnFtg/d2wRyM0jOjJtVCen1EFEmgaFGBERETdXaTO8sXAHAON6RBER7MvoblX7rqzdlUP/dmcWYrZl5PPOslQ+XpkGwLwtGTz53Ra2P3O+Q7lv1u/luR9/s3+ObR/Kf6+Lw9PDNcPZRKTxUogRERFxY8YYpr2/mqT0fKBq3glAq+b+tAnxZ01aDjfR4bTvX1FpY/xLi2qetxli7v+e188NAOCNhTuY/kMSAC9N7Ud5hWF8zyhCA31O+9ki0nQpxIiIiLixlMxCfk46AMAPfxrpcK1v2+Zs3Zd3Rvd/YW6y/bhFkA/f3jWCMS8kUFphA+D2+UX80STzys/bAHjnxkGM6RZ5Rs8UEVGIERERcWMPfbURqAowPVo1c7jWsrkfi5IP1vjO3M3prE3L5f5j9m45VlmFzT5Mbe0j4wmr7lX57alJVFTa6PzQDwD2ADM1rp0CjIg4hVYnExERcVO/pmazIiUbgC6RQTWuRwb7UVhWSWGp4wplf/hwDW8s3EFyRtUQtAVJB/jzrHWUVlSyZlcO/5i9mY9XpnHze78CcPOIDvYAc5iXpwfv3DCIie2P/H3p7wa0cer7iUjTpZ4YERERN7U6NQeAb+4cjpdnzb+3jKyeH9PrsZ9489pYxveIosJmsJmq6y/OTaZ9iwDeXJgCwNfr99EmxJ+9ucUO97ltVMdanz+meyRWui/Jhb7szCykU2Sgs15NRJo4hRgRERE3YbMZ1u/JZWB0KP839zde/WU7oQHe9DvO6mMtqkMMwG0frGFyn1Z8v3G//dyPm9MB6BgRSMrBQgD25hbTvWWwfaEAgMhmfies16fTzmHxtkwig09cTkSkrhRiRERE3MTTc7Yyc8lOBkSHsC6tal+Y8/u0Om75mPAAh89HB5ijvXPDIBYmH+TRbzYT6OPJrGnn0MzPm1d/2U7rkJMHk8hmfkyJbXsKbyIicmIKMSIiIm7AZjPMXLITwB5g3rhmIOf1Pn6IaR8eyPpHxxMS4MP7y1N59JvNADx9SW+GdAjn3BcXAhAdFsB1Q2O4bmiMw/f/dG4X57+IiEgdKMSIiIi4gcXbMwGY1LslQzuF0yLI94QB5rCQgKoJ+SM6t7Cfu3pIewC2PnEepRWVWJY2oxSRhkUhRkREpJH7at0e/vJpIgCT+7bigr6tT/keHVoE4u1pOfS2+Pt44u/j6axqiog4TZ1CjGVZM4GewPfGmKdOUC4K+NEYM8BJ9RMREZGT+HBFGgAeFkzs1fK07mFZFslPTVKvi4g0CifdJ8ayrEsBT2PMUKCjZVknGgD7AuDvrMqJiIjI8W3dn8dlry9jza4c2ocHkPTkJLxrWUq5rhRgRKSxqEtPTDzwWfXxXGAEsO3YQpZljQUKgXRnVU5ERERqtzOzkN/9ZymlFTYAZl4/CB8v7WEtIk2DZYw5cYGqoWSvGGMSLcuaAAw0xkw/powP8BNwCfC1MSa+lvtMA6YBREVFxc6aNcs5b3CKCgoKCAqquWuxNH5qW/em9nVvat9TU1ppuPvnIsps8MBgP4orDP0jG+Y0V7Wte1P7ujdXt++YMWPWGGPiartWl994BRwZIhZE7UPQ7gdeM8bkHq8r2hgzA5gBEBcXZ+Lj4+vwaOdLSEjAVc+W+qW2dW9qX/em9q27zfsOMfmVJQCM7R7JbZcOcnGNTkxt697Uvu6tIbdvXfqd11A1hAygH5BaS5lzgTsty0oA+luW9ZZTaiciIiJ2H63cZQ8wF/Zrzds3NOwAIyJSX+rSE/M1sNiyrNbAJOAKy7KeMsY8fLiAMWbU4WPLshKMMbc4v6oiIiJN18H8Uh76ahMAM6+PY/hR+7qIiDQ1Jw0xxpg8y7LigfHAc8aYdCDxBOXjnVY7ERERAeDamSsB+PL2ocS2D3NxbUREXKtOy+EFvGYAACAASURBVJgYY3KMMZ9VBxgRERGpll1YxvDpvzDhpYXkFpXVWuZki+gUl1Xy+erdvDQvmUpbzbKlFZUkpeczqmuEAoyICHXc7FJERKQps9kMllVzH5WX52/jpfnJ9s/9n5hHyjPn4+FRVW5fbjEvz9/Gp6t3A3DDsBgevaAnNmN4e+lOLurXBn8fT8771yL2HyqpuufPVbsYPDelL5fHteW3jHwCfar+cz2xV1S9v6uISGOgECMiIgL8sHE/wzq3oLm/NwAH8kvIL6mgbag/w6cvYEiHMJ6d0odmflXXN+zJtQeY7i2DKSyrYHd2MR0fnMPUuHY8fUlvhk3/xeEZ7y5L5d1lqfbPz8xJ4oObB7P/UAn/nNKHBUkH+XFz1aCH+77cwH1fbnD4fusQ7SctIgIKMSIi0oQVllbw9fq99gnzAN/eNZzWIf4Mfvpnh7Lfb9zP9xv3A7DqoXF8Vt27EhHsy3+uHkjHFoH0eXwuBaUVfLp6N/O3Zti/269dCP+a2p8nZm9mwW8HHe577cxVAIzpHsnUQdEs35HFlf9dUWt9+7cNOfOXFhFxAwoxIiLS5JSUV1JaYeO2D1azIiXb4dpF/1560u8fDjjje0bx3+uO7MM2548j2Zqex20frCGrsGp+zAc3D2ZklwgA3rlxMHtzixk+/Rf8vD0oKbcBcNuojkQG+wEwtFM4qdMnE3P/9w7PXPXgOEIDfU7zjUVE3ItCjIiINAnGGN5clEJuUTlvLNzhcO3tG+IY0y2S95al8vjsLfbzP/15FGt25fDWkhRSDhZybo8oIoJ9KC6rxM/bk2uHtne4T3R4ANHhAfzvjmFc+toyzu0RZQ8wh7UJ8SfxsQn4e3vy1PdbiO8WwdjuNee6jOoawaLkql6bxfeNIbKZn7P+rxARafQUYkREpElYtC2T6T8k1Th/9JLFNwzvQIXN0DY0gPN6twSgW8tgLh3YhsTduQzpGF6nZw2MDiXxsQn2+TXHOnz+iYt7H/ce7904iPgXErhmSHvahQXU6bkiIk2FQoyIiDQJry3Y7vC5d5tmfHf3yBrlbhnZscY5P2/POgeYw44XYOrKsiwW/m3MGd1DRMRdKcSIiIjbO5BXwsqdVXNfUqdPZu7mdAZEh7q4ViIicroUYkRExO0lpecDcMuIDgBM6NXSldUREZEz5OHqCoiIiDiDMYaS8kp+2pzOjoMFDHxyHi/Nq9rHZVdWIQC3jqo5VExERBof9cSIiIhbeC1hB8//9JvDuZd/3sZfxndlZ2YR/t6eRAb7uqh2IiLiTOqJERGRBssYU+eyH63YVeNcSIA3yRn5vL10JxHBvliW5czqiYiIi6gnRkREXOL7DftJzSrkzjGda1wzxvCXT9czd0sGvxvQBmMgPNCHy2LbsienmPeXp/Knc7sQ7OtNdHgAxWWV7DtUQjM/L+bfO5oPlu+ivNLwxsIdTHhpEQBTBrY9y28oIiL1RSFGRETOGpvN4OFR1Rty58drAbhlZIca5RKSD/L1+n0AfLwyzX7+30ctkzx3SwYAD0zqTsJvVZtCDu4QTmSwH/dO6EZOYZl9U8tx3SP507ld6uGNRETEFRRiRETkrFizK5spry+vcX7T3kPklNjILykn2M+bjXsO8cCXG2kT4s8vfx1NeaUh5WABoQE+/PPHJGLCAyksq+CzX3dTWFbJs0dtYPnQ5B7249BAH774w1C2HyjgisHRZ+UdRUTk7FCIERGRs+Jwz8mxDgeb7knLScsuoqisEoCnL+mNr5cnvl7Qt20IAP++aqD9e49d2It9ucXc+1kiy1OyAOjQItDh3nExYcTFhDn9XURExLU0sV9ERM6KvOJyAL69azgAfds2d7ielJ5vDzADokPqNIeldYg/M2+Io5mfF+O6Rzq5xiIi0lCpJ0ZEROpdRaWNBUkHGdG5BX3bhrDz2fOBquCyJ6eYrxcn8v3OqpCTOn3yKd07wMeLtY+Mx0Mrj4mINBkKMSIiclLJGflc9voy8koqeHhyD24ZeWqbRt7x0VrS80q4f1J3APtSxz1aNaNHq2Z4H/BhaL9u+Hl7nlb9vDw1sEBEpClRiBEREbviskq27D/EgHah9lXElm3P5D8J28krqQDgqe+3Ehbog7enB2O7RxLoe/L/lKzbnQvA+X1aHbfMNee0d8IbiIhIU6AQIyIiAEz/Icm+JHGbEH8+umUIn6xK481FKQA09/emW1Qwq1KzueezRAAsC968JpYJvVpSaTM8M2cr/dqFcFG/1vb7lpRXkldczk3DO+DjpR4TERE5cwoxIiLCwfxSe4AB2JtbTPwLCQ5lHr+oJ5cMaMvN7/7Kz0kHCA/0IauwjGkfrAHgzWtjmblkJ4A9xFRU2rj/yw2UVtgY1in87LyMiIi4PYUYERHhgxW7AHjlygFM7tOK7zfu55GvN9EuzJ9XrxzosHTxm9fG8vmaPVwyoA3//mW7fQPK26rDDEBZhQ0fLw9emJts37RyqEKMiIg4iUKMiIiwLSOfkABvew/KRf1ac0GfVvZ5MUfz8vTgyurNI/8yvit3je3MgbxSRj2/wF7m/FcWM/+e0by9tKpn5u6xnes0d0ZERKQuNDhZRMRNlFXY+PsXG+j/xFx2ZhYCsDu7iAe/2sivqdkUV+/BUpu07CL6twtxOFdbgDmWp4eFn7cn0eEBDue3HyhgV1YhZRU2psa1466xnU/jjURERGqnECMi0siVVlSyO7uI95al8unq3eQWlfOP2ZsBeO6n3/h4ZRqXv7GcHo/+yMwlO5m3JcP+XWMMt76/ms378mgXGnC8R9TJhJ5RDp9HP58AwE0jOuDrdXpLJ4uIiNRGffsiIo1c38fnUlphcziX8NtB9uYWs+NAgcP5J7/bAsD6R8cTEuDDwuSD9lDz+7h2Z1SP164eyK7qMPX+8l32812jgs7oviIiIsdST4yISCNWVmGrEWDuHNMJgOHTf2HL/rxav3f5G8sBeHFeMh4WbP7HRPq0bX5GdfHy9KBTRBBPXNybL/4wFIDJfVrZN7YUERFxFoUYEZFGbMOeqk0k/zaxG+GBPnRoEcjUuGiHMsM6hdP+mDkr2w4UUFRWwZZ9edw6sqPTJ93HxYSx89nz+c/VA516XxEREXDicDLLssKAWGCdMSbTWfcVEZHj+3FTOl4eFtec054/jO6ERdWE/D+N68LLP2/j1pEdeGhyTwD+/cs2woN86RQRxO/fXE7PR38CIK+kol7qph4YERGpL3XqibEsa6ZlWcsty3r4ONdDge+AwcACy7IinFhHERG3sieniOSMfKfc66ct6ZzTMZzm/t54elj2FcVKKqpWImvu720ve9fYLlw5OJpBMaFMPWr+izHGKXURERE5W07aE2NZ1qWApzFmqGVZb1uW1cUYs+2YYn2Be4wxK6oDzUDgp3qor4hIozfin1X7qaROn3za98gtKmPc/y0kq7DMvrfL0W4c1oGt+/O5akj7Gtcsy+Kfl/VlSmxbktLz+N2ANqddDxEREVeoy3CyeOCz6uO5wAjAIcQYYxYCWJY1iqremCecV0URETnW3M0ZZBWWAdA6xL/G9ZbN/Xj/psEnvMfgDmEM7hBWL/UTERGpT3UJMYHA3urjbKp6WWqwqgY/TwVygPJark8DpgFERUWRkJBwGtU9cwUFBS57ttQvta17c5f2Las8MnSrLu9TaTN4HrPp5KbMSl5YXWL/XJGxnYSEnU6royu4S/tKTWpb96b2dW8NuX3rEmIKgMN/zRfEcebRmKpB1XdalvUkcBHw6THXZwAzAOLi4kx8fPxpVvnMJCQk4KpnS/1S27o3d2nfW977FSgCYPTo0Sec/P7lmj3c+3kiy+4fa+9tMcZwwwNzALhpeAfuGNOJFkG+9V7v+uYu7Ss1qW3dm9rXvTXk9q3LxP41VA0hA+gHpB5bwLKsv1uWdV31xxAg1ym1ExFxMwfyS+3HJeVV+7us2ZXD+t01f22+vzwVgP2Hiu3n9uYeOe7QIsAtAoyIiMipqkuI+Rq41rKsF4HfA5sty3rqmDIzqsssAjypmjsjIiJHWZmSxYY9h+yfU7MKKSytYMrry7jqvytqlD868By2L/fIMLJxPaLqp6IiIiIN3EmHkxlj8izLigfGA88ZY9KBxGPK5FRfFxGRWhhjeOCrjQ7nJr28mHZhVcPEisoqHa7N35LB/kNVgWXqmyuIbR/KrGnn2Htl5t8zqtYJ/SIiIk1BnfaJMcbkGGM+qw4wIiJyiuZuySDlYCHhgT58cPORVcN2Zx8ZHvbqz9s4WN37sm53jv18hc2wcmc2/1u7l/TqYNOyuQKMiIg0XXWZ2C8iImfoizV7AFj54DhKK2y1lvm/ecn837xkFt83ptaQcu/nRzrBg3z161tERJquOvXEiIhITRWVNt5blkpyRv5Jy87bkgGAl6cHgScJICOfW0BJ9fCygdEhAJzT8ch+Lj1aNTvdKouIiLgFhRgRkdO0ZHsmj327mce+2XzCcnklVVtnHZ7/Ajjs/RLfLYLpl/Zx+E5hWQUAt8d3BuC20Z1oG+rPxf1b89Udw5xSfxERkcZK4xFERE5RRl4J2zIKeC1hBwCZBaUYY46758u/f9kOwPOX9bOfm/uXUSzdnsnaXTk8OLkHzfy8uf9/Ryb+r0jJok2IP+N7RrHqwXFEBPuy5O9j6/GtREREGg+FGBGRU2CMYewLCRQetZrYtgMFdHhgDk9c3IvrhsbU+M7bS3bSp01zzukYbj/XKSKIThFBDuXXPTKepTsyuevjdaxIyebCfq0BiGzmV2/vIyIi0hhpOJmIyClYvC3TIcBcNSTafvxl9eT9o5VWVFJhM3SKCDzpvUMDfYgOC7B/jq2eDyMiIiKO1BMjIlJHhaUVXPf2KgAGdwijZ6tmPHJBT3YeLGR5Shb5JRU1vrMtowCAEV0i6vSMzpFB9uO4mLATlBQREWm61BMjIlJHP2yq2iprct9WfHbbUB6/qBeeHhafTDuH2PahpGQW1lipbPoPSfh7ezqsLnYiAT5e/Gtqf2Lbh9K9ZbDT30FERMQdKMSIiJzAP2Zv5vmfkgDYm1O1MeVLv+9fo9zLV1Sd+2LNHvYfKua39HzySspZtiOTW0Z2oG1oQI3vHM/vBrThy9uH4eWpX9EiIiK10XAyEZGjrEzJom1YAG1CqpZDfmdpKgA3j+hIel4xLYJ88fGqGS7ahgbQLSqYGYtSmLEoBYDHL+yJzUBP7esiIiLiVAoxIiLVVqdmM3XGCkZ3jWBs90iWbs+0X3tpXjL7ckto1fz4K4V1iQrit6OGkz0+ewvgOM9FREREzpxCjIgI8MycrfYelIXJB1mYfNDhen5JObuzi+gYcfxAckd8Z77bsB8fTw9uHdWB8kpDTHigQoyIiIiTKcSISJNmjGHRtkx7gKnN8M7hfL1+HwA3juhw3HI9WzfjnRsGEdXMj56tNYRMRESkvmjWqIg0actTsri+etlkgPdvGlyjzEPn97QfXzGo3QnvN6Z7pAKMiIhIPVNPjIg0aVv25dmPn5vSl5FdWgAQHRbAm9fG8m3iPrq1DCY0wJvb4zvhrRXDREREXE4hRkSapLeX7OSJ77Y4nPt9dS/L+kfHY2HRPMCbHtUri617dMJZr6OIiIjUTiFGRJqcjLwShwDTLsyfJy7qbf8cEuDjimqJiIhIHSnEiEiTYYzh1vdXM3/rAfu5fu1CeP/GwTQP8HZhzURERORUKMSISJMxf+sBhwCT8sz5eHhYLqyRiIiInA7NUBWRJmPDnlz7ceKjExRgREREGimFGBFpMvbkFNuPm/mrI1pERKSxUogRkSZhzsb9fLVuLwCXDGiDZakXRkREpLFSiBERt5ddWMYdH621f35pan8X1kZERETOlMZTiIhb+mb9Xvy9PWkbGsDmfYfs5z//w1AX1kpEREScQSFGRM6ajLwStmUUMKJLi9P6vs1myCwsZf6WAyxKPsiPm9O5fmh74mLC+GjlLlakZANwbo8o5m/NqPH9KQPbMigm7IzeQURERFxPIUZEzppb3lvNxr2H2Pj4BIL9juzLYrMZEqtXDuvbNgTPWlYNM8bQ8cE5Nc5/vCqN95bvcjhXW4C5cXgMj13Y60xfQURERBoAhRgRcarcojICfLzw8aqacldpM2QVlhIZ7MeW/XkArNmVQ3y3SPt3Plixi8e+3QzAmG4RvHPjYId7VtoM9y48srJYbPtQpo3qyOiuEXh6WHyzfh/PztnKXyd244H/bXT47q0jO3Dfed3x0nLKIiIibkMhRkScqv8T8xw++3t7Ulxe6XDu5vdWExHkS3peCR/ePMQeYABWpGRjjMGyLErKK/k2cR/3fbHBfn3p/WNpE+LvcL/LYttyWWxbCksreOB/Gwnw8WTzPyZqBTIRERE3pRAjIqfkUHE5f/s8keSMfO6f1J0JPVtSbrMx4aVFZBWU1Sh/bIBp2cyP9LwS0vNKALhm5koAJvdtRTM/bz5ZlUbfx+ey+O9jHALRsNZevHTDaKKa+R23boG+XjxxcS/i2ocpwIiIiLgxhRgROSX9/jHXfvyHD9fWuN4+PIAf/jQSm4FNew/xnwXb+dO4LiSl5zO0UzgrUrJ46KtN3HdeN95blkpGXimx7UN55YoBGGP4ZFUa+aUVPPndVvs9/zaxG72sPScMMIddNzTGKe8pIiIiDVedQoxlWTOBnsD3xpinarneHJgFeAKFwFRjTM2/khWRRi2roNR+/MgFPZmzcT9rduUAMDgmjIcv6EF0WAABPlW/Ws7pGM45HcMBiKteFaxTRBAX929DkK8Xd8R35rf0fDpFBFZP5rd476bBXP/2Kr5cuweAFQ+Mo2VzPxIS9pzFNxUREZGG7KQhxrKsSwFPY8xQy7LetiyrizFm2zHFrgZeNMbMsyzrdeA84Nt6qK+IuFDsU/MB+Pt53bl5RAduHtEBgOKySvx9POt8nyDfI796urUMdrg2umsEse1DWbMrhykD29Ky+cl7X0RERKRpqUtPTDzwWfXxXGAE4BBijDGvHfUxAjjgjMqJSMOxIOnIj/Xt8Z0crp1KgKmLz28byo+b0zm3R5RT7ysiIiLuwTLGnLhA1VCyV4wxiZZlTQAGGmOmH6fsUOApY8y4Wq5NA6YBREVFxc6aNeuMK386CgoKCAoKcsmzpX6pbetPaYXhtvlFNPOBJ4b5E+LncdbroPZ1b2pf96W2dW9qX/fm6vYdM2bMGmNMXG3X6tITUwAcXs80CKj1Ty+WZYUBrwJTartujJkBzACIi4sz8fHxdXi08yUkJOCqZ0v9UtvWj+KySq6duRIo4qkpA7ioX2uX1EPt697Uvu5Lbeve1L7urSG3b11CzBqqhpCtAPoBvx1bwLIsH+Bz4AFjzK5jr4tI42KMwWbguZ+SeHNhCgA3De/gsgAjIiIicrS6hJivgcWWZbUGJgFXWJb1lDHm4aPK3AwMBB6yLOsh4HVjzKfOr66I1LdtGflc9/Yq9h8qsZ/r17Y5f5/UzYW1EhERETnipCHGGJNnWVY8MB54zhiTDiQeU+Z14PV6qaGInLGS8kq2ZRTQp21zcovK8Pb0INDX8cd/455DvPxzMvO3Oq7Lse3pSXh7nv05MCIiIiLHU6d9YowxORxZoUxEzoLC0gru+ngtgb5evHLFADw8Tm0H+opKG8kZBXh5WlzwyhLKKm0O168cHM15vVviaVlcM3Ol/XzvNs14ZHJPFvx2kOiwAAUYERERaXDqFGJE5OwbNv0XDhWXA1X7srQLC6jzdwtLK/jTrHU1elWO9smqND5ZleZwbvZdI+jTtjkAQ6o3qRQRERFpaBRiRBqQ1anZeHhYvLZguz3AAIx8bgHdWwbzwc1D+PTXNKLDA7mwbyssq2bvzA8b9/OXz9ZTUn6k52VwTBgzrotly7482oRWLTa4IOkAj8/eAsC/rxrApN6t8DzF3h4RERERV1CIEXExm81w1Vsr2JtbzO7sYodrwzqFs2xHFgBJ6fkMenq+/VpuURnXDY2xfy6vtNHrsZ8oq7DRJTKIpy/pQ1z7UIdhaMM6t7Af3zC8AxHBfuSVlHNBX606JiIiIo2HQoyIC5RWVFJWYSPYz5v7/7eBFSnZtZb728RudIkKZun2TG77YI3DtUe/2UzbUH/SsooY2D6UbRkFlFVU9b785+qBdI0KPmk9JvdtdeYvIyIiInKWKcSIOIHNZrAsah3eVZvfv7mCxN25XDKgDV+t22s/P7RjOOf3bcWwTuGs2plN/3YhWJbFxF4t2fns+QAYA+e+tJCUg4Xc9O7qGvde9LcxRIfXff6MiIiISGOjECNyHF+t20P78EAGRoeesFx2YRkDn5zHgOgQvvjDMPu8kvRDJSTuyWVir5YO5fcfKiZxd271M6oCzJ/P7cLEXi3p0CIQP29PADpFBDl873BAsiz45d547vsikc9W73Eo8+TFvRRgRERExO0pxIgcJTkjn4e/2sSA9iH2neq/u3sEvds0P+53RvzzFwDWpeWyOjWb9uGB5BSVMenlxQB8fOsQhnYMp6C0gpve/RVfL0/7d3996Fy27s9jZJcWde7FOez2+M58tnoPgzuE8cJl/Zj1axqXx7U71VcWERERaXQUYkSqGWO446O1bD9QwKrUI3NULnh1CQOiQ2gbGsB9E7vRJsTfPlneGENRWaW9bFJ6PlNnrHC471X/XYllgbenh33OSkiAN+sfnQBARHDEadW3Q4tA1j4yHm9Pi2A/b+47r/tp3UdERESksdEudiJAWYWNy95YzvYDBYzo3IKerZrx1nVxPHJBT6Cql2V24j5GPreAjg/OYXV1yFlXPSzsxuExBPt68di3mx3uO6RDGFA1j+VwgAF4+nd9nFLvsEAfgv28nXIvERERkcZCPTHSZKzZlcOU15cxrnskL1zej9BAH6BqaeL7v9zAml05AIzuGsGtozrav/fW4hT2HypxuNdlbywn8dEJ/HdRCr5eHvx5XFdi24dy18frAHjz2liGd25BkK8XSel5bNqbx18/T+Ti/q15+YoBZ+mNRURERNyTQoy4vUNF5RSXVzKrenf6n5MOMODJeQyMDmFtWq69nK+XBx/cPIS49o4T+T+7bSjllTY6RgRhsxneWpLCM3OS6PfEXACuHhJN8wBvLujbmkExYYQEeDvMe+neshndWzajc2QQ3VuefNljERERETkxhRhxW5U2w5PfbeHdZan2cyO7tGDxtkwAhwDj5+3B2kfGE+BT80eiXdiR1b48PCxuGdGRj1amsSurCIAnL+5tvx7VzO+49enfLuS030VEREREjlCIEbdkjGH8iwtJySx0OD8gOpQpA9vy50/XA1Urh8WEB+JhWbUGmNp4eFj8cm8887dm0LKZn32Sv4iIiIicHZrYL27FGMNbi1OY+K9FpGQWEt8tgpRnzrdf/31cW343oA19qpdMjmsfRusQf1o2P34PSm08Pao2oOyn3hURERGRs049MeI2yioNcU/NJ6uwzH7uzWtj8fCwWHr/WNIPFdM2tGpo2Ic3D+FgQQk+XsrxIiIiIo2NQoy4jfc2l5FVWAHAveO7MrJrhH2CfZsQf9qE+NvLNg/wpnmAliYWERERaYwUYsQtLEg6wNJ9VQEm6cnz8PP2PMk3RERERKSx0lgaafRWp2Zz47u/AvDNncMVYERERETcnHpipFGrqLTx4rxkfLw8uC/WRxPtRURERJoA9cRIo/bDpnSW7cjikck96ByqHhgRERGRpkAhRhqlDXty2bo/j49XphEZ7MuVg6NdXSUREREROUs0nEwanW8T9/HHT9bZP197Tnu8PJXHRURERJoKhRhpFCpthjcW7qBbVLBDgAGYEtvWRbUSEREREVdQiJEGb2HyQW7/cA1FZZX2c89f1peUzEJW7cymvybzi4iIiDQpCjHiEj9tTie2fSgtgnxrXCuvtOFdPTzsm/V7+dOs9fZrMeEBPHZhL8Z0jzxrdRURERGRhkUhRs667Qfyue2DNVzcvzUvXzHAfv7LNXv4ev1eFm/L5JELehIa4M09nyUCcMOwGB44vzu+XlqBTERERKSpU4iRs8oYw8crdwPwzfp9PDy5Jx+vTGNcj0j++kUixlSV+2lTOi2CfQAYGB3C4xf1clWVRURERKSBUYiRerH/UDH3f7mRP47rTL+2IVTYDH7enizelsnbS3fayw15Zj42Ay/NTwbgvZsG8/6yVH5OOgDA5D6t/r+9ew+zqq73OP7+zjAwDDcBFQRRUQRCFEQ0UTTIvHC8pFZeutfTsTyl1jkdPZmesjx56WrlKT1lnayTml1NLe1R0Mgb3vKGNywQVEaRywAjM8zv/LE30ygo22GYtdea9+t5fNyz9m9mf+f5sPb8vmv91tpc+r4pmfwOkiRJqk42MdoqPvmz+7hv4XLmPNEIQL/etTzwhcP48I/uBmBgfS9WNrfSlv7xPdsN6MO0XYeyoLGpvYk5YMzQbq9dkiRJ1c0mRl3uqaWruG/h8ldtW71uPR+64m7aEkwetQ2/+MQ0rr33WW57opEjJg7n6cbVHLjbUHr3quGIicP58u8f5ZsnTuaYSSMy+i0kSZJUrSpqYiLih8AE4PqU0vmvM2YYcG1K6aAurE8509aW+K/rHwPg4wfvSnPLej5y4Gi+8LtH2s/KfG7WeOpqazh5v504eb+dNvoZOwzqy4ILjuzWuiVJkpQfm21iIuJ4oDalNC0iroiI3VNKT75mzGDgf4F+W6lO5cR3bnmKWx8vNSunHbI7/fuU/ol9cNrO7U3MJD/XRZIkSVugkjMxM4Bryo9vAqYDT75mzHrgROC3XVaZcueBRcvbL9D/1omT2xsYgLeN3Y7T3j6Ght69qK/zNsmSJEnqvEgpvfGA0lKyb6eUHoyIw4ApKaULX2fs7JTSjNd57hTgFIBhw4btc9VVV21R4Z3V1NRE//79M3ntovvpo6/wp4WtAPzgsAZ67yEHEQAAENtJREFU1US3vr7ZFpv5Fpv5FpfZFpv5FlvW+c6cOfPelNLUTT1XyZmYJqBv+XF/oKYzRaSULgcuB5g6dWqaMWNGZ37MFps9ezZZvXYRPbdiLTc98gJf+N0jAOyz82AuOH5Pxg4b0O21mG2xmW+xmW9xmW2xmW+xVXO+lTQx91JaQnYnMAl4fKtWpFyZdsEtr/r6kpMms+PghoyqkSRJUk9QSRPzG+D2iBgBzAJOiojzU0rnbN3SVO2ue3BJ++OvvWcS69vabGAkSZK01W22iUkprYyIGcChwMUppeeBB19n7IwurU5Va1VzC1/6/aMA/P606UwcOSjjiiRJktRTVPQ5MSmll/nHHcrUAzWueoVf3vcssyYOp7UtccjX5wBw2tvH2MBIkiSpW1XUxKhnW9+WOPbSuSxevpYLb5zfvn3HwX35yIGjM6xMkiRJPVGn7jSmnuXmR59n8fK1DOnXu33brInDuf3Mma/aJkmSJHUHmxht1v2LllNXG9z5uUM4dMIwhvTrzaXvnUJE934OjCRJkgQuJ9Nm/PnJF7lszgKm7LQNvXvVcPkH9uGV1jZquvmDLCVJkqQNPBOjdstWr+PBRcs5+jt/5qI/zCelxPt/eBcAZx4xHoCIoL6uNssyJUmS1MN5JkYALGhs4vBv3UbL+gTAQ4tX8PeXVgNwzKQR7L/r0CzLkyRJktrZxPRwi5at4Zp5i7jqnkXtDcwGNzz0PPuNHsIlJ03OqDpJkiRpYzYxPVhzy3oOuvjW9q+HD6zn56fsz+1PNrKquZWv/vFxjtxzBy/glyRJUlWxienBLr9tQfvjU2fsxglTRzF6236M3rYfKSXeNnY7JuwwMMMKJUmSpI3ZxPRgf312BQAjBtVzVvnC/Q0igokjB2VRliRJkvSGvDtZD/Zi0yuMHz6AGz99cNalSJIkSRXzTEwPdPczyxjUt44Xm15h312GMKhvXdYlSZIkSRWzielBbpn/Ao8sXsnXb36ifds/7dknw4okSZKkN88mpgf4/pynWbG2he/Nfrp92w6D6lm5toWDd98uw8okSZKkN88mpuDmPNHIhTfO32j7Hz9zMAPrXUYmSZKk/LGJKZjHnlvJKVfOY9GytXz0wNFcMfeZ9ue2aajjBx+cypSdBlNT42e/SJIkKZ9sYgqkrS0x65Lb27/e0MB8btZ4PnTALqxb3+bZF0mSJOWeTUxBNL3SytsuvhWAE6eO4rgpI1nQuJpdtm3ggN22BaC+rjbLEiVJkqQuYROTQ21tiSeXNjFu+ID2bXMeb+Sl1esAOO+de1BfV8v+uw7NqkRJkiRpq7GJyaEzrn6A6x5cwsD6Xsw751B696rh6cYmAH556gGecZEkSVKh2cRUsSdeWEVD71p2HNzQvu2ppU1c9+ASAFY2t3L/wpe55fGlXDZnAeOGDWCfnQdnVa4kSZLULWqyLkCbtqq5hWMvncv0i27lyRdWAfDsy2t4xzfmAHD628cAcOLld3LZnAUAnLzfqGyKlSRJkrqRTUyVaWtLfOG3D/P+H9zFmnXrATj0m7dx7KVzeejZFe3jDp84/FXfN3nUNpywr02MJEmSis/lZFXgV/c9y54jB3H1PYu44aHnWLKiGYDBDXVc/fFpHPbN23hg0XJO/dl9AHz52IlM2GEg333v3tw6v5EzjxjHsIH1Wf4KkiRJUrexicnIquYWGnr34so7/sYXr3t0k2M+euBoxg4bwJ/Pmsn0i0q3T/6PWeP5wP47A3DUXiM4aq8R3VWyJEmSVBVsYrrZ0pXNvPv7d7Bw2ZqNnhtQ34sLj9+L6WO2JWqgf+9SPDsObuD606fT3NLmhfuSJEnq8WxitrJHlqxg3LAB9KotXX706/sXb9TAXHLSZO5fuJzPHj6O/n02HckeIwZt9VolSZKkPLCJ2Ypue6KRD15xN6fO2I0zDx/Hbx9YwgU3zmf37ftz3WnT+eofH+fDB+zCqCENvHPyyKzLlSRJknLBJqaLta5v44q5z/C3l9Zw86MvAPC92U/z0zv/zqrmVgDOOmI89XW1nHvUhCxLlSRJknLJJqaLXTPvWb5yw/yNtq9qbmW7AX24/rTpbO+dxCRJkqROs4npIm1tiZqaYO7TL75q+7WfmMbK5hbWtbax7y5DGNq/T0YVSpIkScVQURMTET8EJgDXp5TO7+yYarRiTQsNfWqpq+38535+46bH+eldCznnyLdwzzPLOHrSCA4asy0/v2che+44iD69aruwYkmSJKln22wTExHHA7UppWkRcUVE7J5SevLNjqlGi5ev5ahv38744QO5Y8FLAGzTUMcxk0Zw3jF7EBEbfc+KtS0sX7OOnYf246mlTZxy5TwWNK4G4F+veRCA/XYZzAn7juKEfUd13y8jSZIk9RCRUnrjARHfBv6QUrohIk4C+qaUftSJMacApwAMGzZsn6uuuqorf4+KNTU10b9/fwBSSlzx8DpuX9y6ybFn7VvP+CE1PL86ccdzrTz60nqeWt4GQO9aWLe+NK424FN79+GS+15hSH1w3gF9GdB74wZIW1fHbFU85lts5ltcZlts5ltsWec7c+bMe1NKUzf1XCXLyfoBi8uPlwFTOjMmpXQ5cDnA1KlT04wZMyp46a43e/ZsOr72jBmJ797yFCMH92XCiIE89txKPnN16YzKRfc0v+7P2dDAnHHI7hw/ZSQ7D+3Hx45poXevGpePZeS12apYzLfYzLe4zLbYzLfYqjnfSpqYJqBv+XF/YFMXj1QypipFBKcdsnv71+OHD+TYySOZ/UQjH/nRPe3bPzZ9NE83NnHyfjux/25DeWzJSvYYOehVH045oL6uW2uXJEmSeqJKmph7genAncAk4PFOjsmNiGDmuO25++xDWL62hd2377/R9TFv3XVoRtVJkiRJPVslTcxvgNsjYgQwCzgpIs5PKZ3zBmP27/pSu9/2A+v9TBdJkiSpymx22VdKaSUwg9JZlpkppQdf08BsasyKri9VkiRJkir8nJiU0svANVs6RpIkSZK2VG4uwJckSZIksImRJEmSlDM2MZIkSZJyxSZGkiRJUq7YxEiSJEnKFZsYSZIkSbliEyNJkiQpV2xiJEmSJOWKTYwkSZKkXImUUve/aEQj8Pduf+GSbYEXM3ptbV1mW2zmW2zmW1xmW2zmW2xZ57tzSmm7TT2RSROTpYiYl1KamnUd6npmW2zmW2zmW1xmW2zmW2zVnK/LySRJkiTlik2MJEmSpFzpiU3M5VkXoK3GbIvNfIvNfIvLbIvNfIutavPtcdfESJIkScq3nngmRpIkSVKO2cRIkiRJypVCNzERUejfr6eLiNqsa5DUOb4/F1dERNY1aOtx3y2uvM2rCvcPMSIOiYizI2JoSqkt63rUdaJku4j4EUBKaX3WNalrRcSeEVGV96PXlouIQyPiYxHR1/fnYomImRFxJEDyYtvCcW5VTHmfVxWqiYmIrwL/AgwDPhMRIzMuSV2o/IexBpgVEe8GjwgV0CeBwVkXoa4XERcDnwOmAP9W3uYR+xzbkF9EfAo4G3hHRJwREWOzrUxdoUO+X8O5VaFsyDbv86rcFFqhZcA/A2cCewD+gSyIDjvVcOA54MMR0ZBSasvb6U9tLCLqyg8XAodHxDERcWBEDCo/776cf0spvT+fQWmyW+cR+/yKiD7844BDX+DKlNJnKOV8dEQMyaw4bbHX5NsInIJzq0IoZzuow6YR5HRelesmJiImRsRXyo8bgHnAypTSK5QaGo8G5VjHfDtoAS4F7qQ0Gcrd6U+VdMw3pdRS3jwTWF1+/C7grPLzTnZzZhPvzwAfB04CngA+EBEzMipPWyAiPgL8HvhqRMwEWoGa8sGmucB64PAMS9QW6JDvhRFxGHA/sNy5Vf51yPbrEXFw+QBhM/Df5HBelesmBhgDvC8iJqaU1qSUbk4ptUZEP2BsSukWeNVRXuXLhnwndFiDuwOwS0rpfOC4iLg6InbMrkRtgfZ8O2y7F3gxpfQ74GvASJcu5NaGfPdMKa0BfgLcBewCXAhsAxxWfr9WTkTEdsAxwKnAw8BbgL8BewNDgcXA34ERHZpX5cRr8n0M2DuldFNKab1zq3x7TbZ/BaaXDxCOAnbO47wqV01MROwUESdGxA7lTQ3Ar4HPdxhTR+mo0B0RMS4izgf27/5q9Wa9Qb7ndhi2HlgXEd8A+lNqaJ7t5lLVCRXmu4ZSvjXAjpQ+kHdxN5eqTtjc+3NKaSml5Sm3pJQWUDr6NzKltHqTP1BVo0O2wyntl9eklJ4C5gDvTin9mlKeRwK1wArgoHLzqir3BvneSinTDUu6W4G/OLfKj83su7PKw9YALRHxdXI2r8pNExMRE4Frgb2Az0fEfsBvU0qfpnQa+z3QvixlLPBZ4DJgaUrp9ozKVoUqyPeE8tBewHHAwymlCcCXyt/vGt0qVkG+J5aH3gBsC/wYuJjSElFVuc3kGx323xeAT0fEVcBFlJYeqYp1yHYSpb+rA1JKPy8/vQx4pvz4CmAApaUqRwOPdHOp6oTN5LscmA9QXg0xFvh3nFvlQqXZUuoFjgUeydu8qlfWBWxORLyL0qTmNmB+SunzEXE4cCClI32zKS07OS8iflne0QYD5wHfSSkty6ZyVeJN5PuliLg2pfSniDg4pbQKIKV0ffn/XjNRhd7k/vuLlNK8iLgXmE6pUX05o9JVgU7sv9dFxGOUjuCeXj47oyq0iWzPLme7X0S0ppT+AoykdNYFoA74H0rLyVYBD2RQtir0JvJdVR6/F6VlR+cC33NuVb3ebLbA88CRKaUlkK95VdU2MRHRC/hh+cu1wK7A8xExGPgzMBCYFhHzUkr3RMQzlBqXc4G5KaXbsqhblelEvguALwL/mVJaFRE13qu+enVy//0ipXwT4BG+KtbJ/fc84NzyUoansqhbm1dhtm+LiLsoXQczpHxmbSlwdkrpVxmUrQp1It+hEXE1peucLtwwwVX12YJ99wXgnDzOq6p5OVkb8HRK6UOUTm1Np7Seb0x5DfV8oDeli0Sh1LzcCvm5q0IP15l8Z2/45rztaD3QFuWrqtfp92dVvUqybaB0JHcYcADwfyml01NKTRnVrMp1Jt+fpZTO9OxL1evsvntGSmlVHudV1dzE1FC68IjyKa5HgSXAURExhlIYMyl1m6SUlm24Y4ZywXyLzXyLzXyLq5JsDyqP/XFKaWz5boLKB/Mtrh6XbdUuJ0sptVIOIyJGAaNTSu+IiJOALwMvU1p765GfHDLfYjPfYjPf4qow2+eA5pTSwuwqVWeYb3H1xGyrtonZoHxbvxZKt/V7CzAO+COlMBallF7Isj5tGfMtNvMtNvMtrgqy9aYMOWa+xdWTso0c3HyAiDgG+A1wE6X1ez/JuCR1IfMtNvMtNvMtLrMtNvMtrp6SbV6amJnAW4FvpJTWZV2Pupb5Fpv5Fpv5FpfZFpv5FldPyTYvTUzk4X7V6hzzLTbzLTbzLS6zLTbzLa6ekm0umhhJkiRJ2qCab7EsSZIkSRuxiZEkSZKUKzYxkqTMRcTkiJicdR2SpHywiZEkVYPJ5f8kSdosL+yXJGUqIi4Ajit/uTildEiW9UiSqp9NjCQpcxHxYYCU0o+zrUSSlAcuJ5MkSZKUKzYxkqRqsBZogNIHtWVciySpytnESJKqwc3A8RExFzgo62IkSdXNa2IkSZIk5YpnYiRJkiTlik2MJEmSpFyxiZEkSZKUKzYxkiRJknLFJkaSJElSrtjESJIkScoVmxhJkiRJufL/jDzOMLtI18kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2['滚动找RP'].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "153d4013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acac502f40>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9vEXfjyv3OO53f24q909c5aoQAVi8IwPAaa+W+89r56pwpIHRnBgRERGROuSrpbsAKLMb3pixhWGdIjEGdmcWsHFvDpeNXwDAr6tT6d2qMQkdIrhuwmJahvrTNTqYm89uzYHcYjo3C2ZfdhFNgnyw1fDwri37c5m1OY1QX4tnLuxEVkEJ57RvwmW9omv0PSJVURIjIiIiUgfY7YYbP17C3K0HHdd8PW08+5fO/GfmVqZv3M/IN+c6PfPML+t5hvUA7MooYN62g4xP3A7ANX1b8NWSFB4Y2o70vBI+W7QTgDvPbcNjIzuecpzldsPw1+cA0CvShq+XjfHX9Trl+kROhYaTiYiIiLjYgm0H6fj0ZEcC8+bVZzG4XThPje5E8xA/hneOcip/d3wsH9/U57h1frUkBYA3pm91JDCAYwL+qUrLLXIct26kXyXFNdQTIyIiInKaNu/LJaqRL/+etpkWjf25/Zw21X52zG8b+HBeEgDNQ/yY988ELMviorOaO8okdIhg0wsjmDAviX4xofRuHQpULGe8clcmXjYPVu7K4u6EWD5duJNxf2w67jsP5hUTHuhzCl8Ky5IzAXhiVEfalO08QWmR2qEkRkREROQU7Mkq5OaPl/D4qI7c9PFSp3sJcRG0jQg8YR3lduNIYABHAlMZXy8b9yS0dboW2ciXEV2aAjCkYyQAfzs3llB/byat3cuHN/Zm9uYDrEvNZnS3ZkzfuJ9xf2xi0Y50RndrdlLfu25PNu8kbnMsNnDDwFYsnLfrpOoQqSnqAxQRERGphuKycga+NIMr310IwLfLUtiyP++YBAbg5k+W8PvavSzfmXHcOmMf/91xvOmFETW2PPGVfVrwv1v64mXzYGinSB4Y2p62EYFc269iJbF7v1zJ5n25xzyXX1zGuj3ZldY5+u15jgSmS/NG+HjWvc03peFQEiMiIiJyAtsP5NHhycmkZhexJDmD1o9O4o3pW53K3DYohm/uHABASkYhd3+xgsvGLyQjv4QJ85L4aF4SRaXlldb//V0D8PWq/aQgyNeLmwa2BuD8N+Ywf9tBjDGUldvJzC/hgrfmMvrteczalOb0XEpGgeP4xUu68tt9g2s9VpHj0XAyERERkRN4/tcNlV5/8ZKuPP7jWj64oTfDOlUM57r57NZ8PD/ZUeavHyxia1oe5XbDlPX7eGREHB/NT2LsxV0cZTo3C67V+I/00PD2rNuTzbKdmSxLzmTi0hR+XZ3qVObmTyp6l5Y8MYTiUjuXv1uxrPOCR8+jWYjfGYtVpCpKYkRERESOo6TMzpKkDC7vFc2rV3QnJaOAwS/PAiqWMf5rv5ZO5Ud0juLj+ckEeNvILyln06FhW7FNAliVksUdny4jPb+E+dsqViIbf23PM9IL86cgXy++u2sg/V+cwevTtxy37EX/mc/e7MOrkSmBkbpCw8lEREREjuPj+UkUlpY7elpahPrz490D+er2/pXOYenXJoyZD53LiqeH8dFNvR3XL+sVTXGZnfT8EgCyCkoBHCuNnWmPjowDwMtW9TycIxOYZy/sVOsxiVSXkhgRERGR4/h6aQqxTQI4Ly7Cca1Hy8YMiA2r8pk2TQLx8bTRIaoRzYJ9efuaHlzWs/Ld7MMDvWs85uq46KxmPDGqI7Mejndc69K8Ec9f1NkRa/vIwCPKNz+6ChGX0XAyERERkSrY7Ybk9HxuH9wGL9vJ/+23eYgfCx4bUuX91c8Mr7EVyU6WZVmO/WzmP3oeG1Jz6No8mKhgXxgAnh4WXy+r2DDz/4a2p3GAa5ItkcooiRERERE5ZPqG/WxJy3UkLXO3HcRuoFVYQI3U/+jIOF6evInv7hpIbHggwX5eNVLv6Woe4kfzo+a7tAzzdxwP7RRx9CMiLqUkRkRERISKXpeP5iexYHs6P63cQ3igD+tTc/D3tjG6e9Maecffzo3lb+fG1khdte26fq14Zcpm3rm25xldPU2kOpTEiIiISIO2PjUbY+DOz5azJ6uQ5iF+bNmfx5b9eQBc2L0ZjXzrRo/JmRTs70XyuAtcHYZIpZTEiIiISINVVFrOBW/Nc7p266AYbhzYmov/O5+1e7L5+5C2LopORKqiJEZEREQarOT0fKfzTk0bcW3/ltg8LL6+sz+pWUW0jQis4mkRcRUlMSIiItJgbU87nMR0iw7ml3sHOc79vT2VwIjUUUpiRERExO0VlZYT99Rkx/nZbcP4zzU9uefLFQA8OKw91/Vv5arwROQkKYkRERERt7dmd7bT+fxt6fR4YZrj/N6Etnh4uGa/FhE5eSe/a5OIiIhIPbM0OcNx/NjIOKd7kx8YrARGpJ5RT4yIiIi4vZW7smjTJICZD8UDUGY3vDJlM/ckxNIhMsi1wYnISatWEmNZ1gSgEzDJGDOmkvuewI5D/wO4zxiztsaiFBERETlFy3dmsColix4tQxzX7kloyz0JWjpZpL464XAyy7IuBWzGmAFAG8uy2lVSrBvwlTEm/tD/lMCIiIiIy30yP4nLxi/kYF4xg9qGuzocEakh1ZkTEw98c+h4KjCokjL9gdGWZS2xLGvCoZ4ZEREREZcpLivn2V83ANA8xI/LekW7OCIRqSmWMeb4BSqGkr1ljFltWdZwoKcxZtxRZfoAu40xey3L+hT4zhjzy1Fl7gDuAIiMjOw1ceLEmvyOasvLyyMwUGu+uyO1rXtT+7o3ta/7cmXbLt1Xxn9XFXNHNx8GNtPfV2uDfnbdm6vbNyEhYbkxpndl96rzE50H+B06DqTy3ps1xpjiQ8fLgGOGnBlj3gfeB+jdu7eJj4+vxqtrXmJiIq56t9Quta17U/u6N7Wv+zqTbZueV8zMTWn0bNWY2CaB3PHEHwCMHNSLHi0bn5EYGhr97Lq3uty+1UlillMxhGwR0B3YXEmZzyzLGgusAy4GXqyxCEVEREROIC2niL4vznCcT3ngHErK7QBKYETcUHXmxPwEXG9Z1mvAlcB6y7KOXqHseeAzYBWw0BgzvWbDFBEREana3V+scDo//405AHx/1wBXhCMiteyESYwxJoeKyf2LgARjzGpjzJNHlVlnjOlmjOlqjHmidkIVEREROdbkdXtZtjOTB4e1J3ncBUQ3rhgF72Wz6No85ARPi0h9VJ2eGIwxmcaYb4wx+2o7IBEREZHqSj6Yz98+r+iFubZfSwD+PqRiam6gjyfentX6VUdE6hkt1SEiIiL1kt1uuOOzZY7zsEAfAC7vFU2Z3dAsxK+qR0WknlMSIyIiIvXS7sxCtuzPY3C7cF65vLvjumVZXNO3pQsjE5Hapj5WERERqXcWbD/IOa/MAuCBoe2ICvZ1cUQiciYpiREREZF6538Lkh3Hmrwv0vAoiREREZF6pai0nJW7sgBIfDhek/dFGiD91IuIiEi9MnXDftJyi/n0lr60Dg9wdTgi4gJKYkRERKTe2JVewP1frcTH04OBsWGuDkdEXESrk4mIiEidZoxh/OztBPt5MWnNXgCah/jhadPfYkUaKiUxIiIiUqdtS8vj5cmbna69dU0PF0UjInWBkhgRERGp075fscdx/N71vRgSF6FeGJEGTkmMiIiI1Fl5xWW8O3s7AJPuH0TnZsEujkhE6gL9GUNERETqlOyCUt6asZW92YX8tjoVgIeGtVcCIyIO6okRERGROuXLJbt4bdoWXpu2xXFtWOdIF0YkInWNkhgRERGpE75cvAsfTw9W7sp0uv76Vd2Ji2rkoqhEpC5SEiMiIiIuV1pu5/Ef1zrOB8aG0S4ikGkb9jO4XRMXRiYidZGSGBEREXGZyev28ce6vVzWM9rpeuMAb567qAtPX9gZm4flouhEpK5SEiMiItLAGGMoLTd42Swsy7UJwt8+Xw7Az6tSsXlYlNsNANvT8gCUwIhIpbQ6mYiISAOxaV8O572aSMxjv9P+yT/45/drXBpPdkGp03m53TD/0fMAuH5AK1eEJCL1hJIYERGRBqC4rJwRb8xlx8F8x7Vvlu2msKT8jMeSklFASkYB7yRuA+CCbk0BePKCjjQP8SPppVFc209JjIhUTcPJREREGoBZm9IAGBIXwdBOkWzdn8dH85PYk1VA24igGnvPrvQCAn09CQ3wrvR+XnEZg1+e5TgPC/Dm31d0585z2tAtOgTA5UPcRKTuU0+MiIhIA7AnqwiAp0Z34pq+Lbm8V8VE+nV7cmqkfmMMz/+6gXNemUXPF6axIfXYeotKy7n9f8sc575eHnx6a198vWyOBEZEpDrUEyMiIuLmXpu6mbdmVgzdim7sB0CHqCACvG0s35nJxT2an1b9/521jVembHa6NuqtuVzTtyXDG1dM1D+QW0yfsdMBuCchliBfL0Z1aUrLMP/TereINExKYkRERNxYWm6RI4F57crueNoqBmHYPCzOahnCqpSs06p/W1quUwITEx5A0qF5N18t2YW9gzcDSssZ/vpsAP7SvRn/OD/utN4pIqIkRkRExI29OX0rAJ/f2o9B7cKd7rUM9Wfahv2nVf9TP613HH95ez8Gxobzw4rdbEjN4cN5SXy9uYSvn5rsKPPSpV1P630iIqA5MSIiIm5rxa5Mvli8C4CerY6dcxIe6ENGfoljb5Y/fbYwmSvfW4gx5phnjpRVUMKynRkAJL00ioGxFUnSpT2jeXJ0J3y9nH/NeOuaHgT46O+nInL69C+JiIiIm1qSVJFg/O+Wvvh7H/t/+U2CfLAbyMgvoUmQj+P6Uz9X9K5s2JtD52bBLNh+kPGJ23nxkq78sjqVN2dspaTM7ij/zrU9K11RbMkTQ5k5ey4PzCoEoG2TwBr9PhFpuJTEiIiIuJHCknL8vG08/O1qvlu+m0a+npzbvkmlZcMDKxKXPmOn42Wz+OPvg2ka7Oe4//LkzQyIDWPcH5sAnJZGPlJV9Tfy9SLEx4O74mMZn7idmPCA0/k0EREHJTEiIiJu4pfVqdz/1Ur6tg5lSXJFL8yF3ZtVWT7Ez8txXFpuGPraHKf7s7ccYPaWA7QM9WdXRoHj+rhLuzJ/ezq/rk4lPND7hEPEHjm/A/ef1w4/b9upfJaIyDGUxIiIiLgBYwxP/LAWwJHAfHxzHxI6RFT5TJfoYDw9LM5qEcLaPdkUHzFE7Ehf3t6PtbuzGT97OwNiw7iqTwuu7tuSGwa0ws/rxImJZVlKYESkRimJERERcQP7c4rJLS6jbUQgdwxuQ3Son2OifVUa+Xqx7cVRAExdv487PlsOwNxHEoDDw8eiG/sT3difkV2bOj3fp3VoTX+GiEi1KIkRERGp535fu5e7v1gBwN/OjeXyXtEnXUf/2DAAPCxoEVqxAWXSS6OwH3+BMhERl6hWEmNZ1gSgEzDJGDPmOOUigcnGmB41FJ+IiIicwLuztzuOz4urevjY8TTy9WLMxV3o2DTIcc2yLGzHLjomIuJyJ0xiLMu6FLAZYwZYlvWRZVntjDFbqyj+KuBXxT0RERGpQQUlZbw2dQtrdmcDsPrp4QT7e53gqapd179VTYUmIlKrrBNtZGVZ1ltU9K78blnW1YCfMebjSsqdB1wJxBlj4iu5fwdwB0BkZGSviRMn1kD4Jy8vL4/AQK1T747Utu5N7eve1L4nr6jM8OLiInblVkzGf7yfL+0b173J82pb96b2dW+ubt+EhITlxpjeld2rznCyAGDPoeMMoOfRBSzL8gaeAi4BfqqsEmPM+8D7AL179zbx8fHVeHXNS0xMxFXvltqltnVval/3pvY9ecNem82uXDv3D2lHkI8ntw2OqXTDSVdT27o3ta97q8vtW50kJo/DQ8QCAY9KyjwKvGOMyaqL/4CKiIi4k7GTNrA1LQ+AB4e1d3E0IiJnXmUJydGWA4MOHXcHkispMxS4x7KsROAsy7I+rJHoRERExGF/ThFdn5nCB3OT8Pe2sW3sSFeHJCLiEtXpifkJmGtZVjNgJHC1ZVljjDFP/lnAGHPOn8eWZSUaY26r+VBFREQarnK74ZZPljr2gnn58m542qrzt0gREfdzwiTGGJNjWVY8MAx42RizD1h9nPLxNRadiIiIAPDy5E2sT83h6j4tGHtJV2weGr4tIg1Xtf6EY4zJNMZ8cyiBERERkUPK7YYfVuxmwryk06pnW1oes7ccqPSe3W54bx00UuwAACAASURBVM4OAJ65sLMSGBFp8Kq12aWIiIgca+WuTN6dvZ0p6/cDMGlNKj/cfbZTmfS8Ysb9sYkmQT7cPrgNjQO8AdiWlkvL0AA8LPjHd2v4cWXFQqBRjXy5dVAMF3RrSrOQinV1sgtLAXh6dCf8vOveMsoiImeakhgREWnwjDFMmJfEqK6HE4ei0nKMAT9vG+MTt+Pj6cEtg2Icz+QWlXLJOwuc6lmxK4tL35nPg8M6MKhdOHd8uoypG/Y77r+TuJ3uLULYnVFAen4Jt5wdw4Xdm/Ljyj1c2rM5v6xKZV9OEWN/38jY3zdyV3wsv61JJSWjEIDwIJ8z8F9DRKTuUxIjIiIN1q70Al6YtIFphxKNMZM28sPdA4mLCqLT01MAaB7ix56siiTi+d82cFd8LA8Na8/ni3Y56vn4pj70at2Ybs9OZcWuLK6bsJj7zmvrlMD8aXVKluP4o/lJfL20op57E9ry7yu689TP6xx1j0/c7vRsXFRQDX69iEj9pSRGREQanMz8EnZnFnLhf+Ydc+/So3pX/kxg/jQ+cTvfLkshPb+Ers2DGX9dT6Ib+wPw6hXd+XrpLpYmZ/L2zG0AXHRWMx4Y2p6Y8AByikr5v4mrmLEpzVFffkk5cVFBtAz1x7IsxlzclUdGxNHt2alO733hos60j1QSIyICSmJERKSBsNsNL/2xkUU7Mli7J9tx3d/bxtT/O4foxv58OHcHYyZtdNy7bVAMv65JZX9OsVNdB/NKCPL15Np+LR0JDMDlvaK5vFc0783ezkt/bALgX5d1w9erYh5LI18vJtzUh9/WpNIy1J+//Gc+Q+IimHBTH6f6G/l6OZ2/cHEXruvXsmb+Q4iIuAElMSIi0iBMWruXD+Y6ryDWv00o713fm2C/iqTh1kEx2I2heYg/I7pEYfOweHJ0J9Jyivhp1R5uH9wGyzrxymC3DW6Dv7eNC7o1cyQwRxrdrRkACx49j8b+3pXWMfmBwYx4Yy7Rjf24vn+rk/1cERG3piRGREQahH9P3ex0PqprFO9c28vpmmVZ3HFO7DHPRjTyrfR6VWweFtcPaH3Ccn8uIlCZuKhGfHFbP1qG+ldZRkSkoVISIyIibi8lo4Dk9AIAksddwLo92cSEB7g4qhM7u224q0MQEamTlMSIiIjb25aWB8A/R8QB0KV5sCvDERGR0+Th6gBERERqgjGGotJyFmw/SEpGAV2fmcKLv1dM0t+Zng9UTLwXEZH6Tz0xIiLiFt5J3M4rU5znvbw/ZwePj+pIcnoBAd42wgMrn0QvIiL1i3piRETELXy5eNcx14J8PVmdksUnC5IJDfSu1spiIiJS96knRkREXGJ3ZgHZhaV0blb5/JR/fLua5PR8ruvfivaRQeQVl3FWixB2ZxbybuJ2HhjWjsb+3vh62cgvLmNPViGN/b2Y9uC5fL5oJ8bAmzO2ctF/5wNwXT8tUywi4i6UxIiIiEuMeGMuecVlJL006ph7G1Jz+Hb5bgCWJmc6rvt4elBcZgfg62UpAHx+az8+nl+x/0uvVqGEB/rwwND25BaV8uaMrQDccnYMd55b/SWSRUSkblMSIyIiZ0RKRgEj35zLNX1b0C06hLziMgDH0sd/Ki4r55/frwHgl3vPZndmIUkH87HbDXO2HqB9ZBCZBSX8vnYfANdNWOx49qnRHR3HQb5eLHl8CLnFZcQ2CaztzxMRkTNISYyIiJwR3y7fTV5xGR/MTXK6Pua3DczYlM//lW3F39vG2EMrit06KIZu0SF0iw5xlL1vSDvHsTGGTxYk89yvGxzXWoU57/0S0ciXiNr4GBERcSklMSIickaUlVcMA+sWHcya3dmO6zM2pQHw+vQtTuXvSWh73Posy+Lms2MY2aUp/V+aQUKHJjUcsYiI1FVKYkREpFrKyu2U2Q2+XraTftYYw+9r9zIwNowvb+/P/G0HaRsRyLLkTD5blMyiHRmOskufGEqAjw1/7+r9X1RUsC/bxo7UymMiIg2IkhgRETmhZckZXP7uQgCu69+SMRd3Pannx/2xieT0Au6Or+hdObttOAAXdGvKBd2aMm3mLCbuCiQy2JcmQT4nHZ+nTTsGiIg0JEpiREQEgAO5xfy4cje7Mwt5YGh7QgMqNoZ8+NvVfHdopTCAzxftomPTRuzLLuLK3i1oEep/wrp/XpUKwIiuUZXe9/KwmHBTnxr4ChERaQiUxIiICACXv7uAnYdWCvt04U5uHNCK/y3c6bjfs2UI/dqEMT5xO0/8uA6At2duY/y1PRnZtSkAi3ak0zzEzymxKbcbCkvLuaJXNI18vc7gF4mIiLtS/7uIiLBuT7YjgRneKRLAKYG5+ezWvHhpV/45Io6hHSvW++oWXbFJ5V1frKD1o5NIPpjP1e8vIuHVRKe6P1+0k+zCUscQMhERkdOlnhgREeH7FRXDxRIfjqd1eAAlZXZenbqZLs2DubBbU6dJ8+9d35vpG/czJC6CV6du4d3Z2wEY/fY8AMrsxlH222UpPPPLegAlMSIiUmPUEyMi4kaMMRzILXa6tjM9n+Ky8uM+tzO9gLioIFqHV+yz4u3pweOjOvKX7s2OWfXL5mFxfucoPG0ePDy8PYseG8JDw9o7Nq8EeHvGVgAe+2EtAKO6Rp3ShH0REZHKKIkREXETe7MLGThuJn3GTmftoX1Yxidu59xXErnm/UV8szSl0ufsdsOqlKxqTdA/mqfNg6hgX6dNKAH+PW0L2QWl2I0hLMCbp0d3PvkPEhERqYKSGBGRei4jv4Svl+7i9k+XsTe7CIAL/zOPsnI7c7YcAGDFriwe+X4NrR+dxL1frnA8W1hSTpvHfycjv4RWp5DEHCk80NvpfOSbc7AbeP+GXkQF+55W3SIiIkfSnBgRkXqu5wvTKr3+j+/WsHBH+jHXf1uzl6v7HGRQu3A+W5QMgK+XB7cNbnNacfxw19l8uzyFt2duAyD1UELVPTrktOoVERE5mnpiRETqsSPnofxpwo29Afhx5Z4qn7tuwmKMMXwwN4lerRqz6YWRp91b0jLMn4eGdyB53AX867KKzTA9PSxtRCkiIjVOPTEiIvXYwu0VPS1f3NaP3KIySsvtnNO+ieN+35hQPru1L7lFZQx/fQ6dmzUip7CU1buzeXPGVg7kFnNNnxY1HtdVfVrSLyZMw8hERKRW1FgSY1lWKNALWGmMOVhT9YqISNV+WrmHRr6e9I0JxeuIHo97E9ryn1nb6B8Tio+nDZ9AG7Mejsfb5kFJuZ3uz03ljekVK4ht2JtTK7H9udKZiIhITatWH79lWRMsy1poWdaTVdxvDPwG9AVmWZbVpLJyIiJSc3YcyGPu1gMkxEU4JTAAHaKCAOjVOtRxLdjPCz9vG8F+Xqx+Zjhetoqlk6/sXfM9MSIiIrXphD0xlmVdCtiMMQMsy/rIsqx2xpitRxXrBjxojFl0KKHpCUyphXhFROq92z9dxs70fKb+37mnXEdqViHn/Xs2AG2bBB5zf3S3pnRpHkxMFb0hwX5ebB076pTfLyIi4krVGU4WD3xz6HgqMAhwSmKMMbMBLMs6h4remOdrLkQREfcybcP+065jSVKG47iyeSeWZVWZwIiIiNR31UliAoA/l7jJoKKX5RhWxZbOVwGZQGkl9+8A7gCIjIwkMTHxFMI9fXl5eS57t9Quta17c8f2rc73GGOo+Of1sKX7yvjvqmIAogMtfDK2kZi4vTZCPGPcsX2lgtrWval93Vtdbt/qJDF5gN+h40CqmEdjjDHAPZZlvQD8Bfj6qPvvA+8D9O7d28THx59iyKcnMTERV71bapfa1r25S/u+9PtGYAfACb/np5V7eODrVSx87DyaBlf8M2yM4abHfgdg7CVduLZfq9oM94xxl/aVY6lt3Zva173V5fatzsT+5VQMIQPoDiQfXcCyrH9alnXDodMQIKtGohMRcTOzNqc5jovLygH4Y+1eplcyxOzj+UkA7MksdFxLyTh8bDe1FaWIiEjdVp0k5ifgesuyXgOuBNZbljXmqDLvHyozB7BRMXdGRESOsD41my378xznG/fmklVQwl1frOC2T5c5lbXbDat3Zx9TR2p2RRLjZbP4S/dmtRuwiIhIHXXC4WTGmBzLsuKBYcDLxph9wOqjymQeui8iIlWYMDfJ6fzi/84nyPfwP8N2u8HDo2L+y7fLUxzXL393Id1bhPDDXQPZl10EwOQHziHYz+sMRC0iIlL3VGufGGNMpjHmm0MJjIiInKRFO9L5YWXFGilvXn2W43puUZnj+Imf1jqSlAO5xU7Pr07J4u2ZW0k6mA9AVKNjVyQTERFpKKozsV9ERE7T/xYkAzDnHwk0Dqi8B+WrJSl8tSSF8df2pNGhXhYvm0VpecXklzemH17dPsBH/3yLiEjDVa2eGBEROVa53fDfWdvYkJpzwrJ/rKvoyG4Z5k+Q7/GHgd31xQoKSyom/Q9qGw7ANX1bOO6f277JqYYsIiLiFvSnPBGRU5S4OY1Xpmxm5a5MPryxT5Xl0nIrhoi1aVL55pM3DGhFl+bBPPLdmiOeKcbDgsdGdWT17mxuHdSGjk0bERfViN6tGtfsh4iIiNQzSmJERE7S3uxC1u3J4Ykf1wKwLS2PTftyaB7iV2kvy1sztmJZ8OZVPRzXfrx7ILM2pWGAO85pg4+nzSmJ+WPtXjo1a0T7yCBWPFWxbkrbiMDa/TAREZF6QkmMiMhJGjhuJuaIPVqS0wsY8cZcbhrYmmf/0vmY8rM2HaBv61C6Rgc7rvVo2ZgeLZ17VLaNHcnipAyu/XAxqdlFjOjStNa+QUREpD7TnBgRkZMwe8sBpwTmnyPiHMdZBSXHlC8uK2dPViG9qjEEzNPmQfMQP8d53xgNGxMREamMemJERKqpsKScGz9aAsDA2DAu6NaUv/ZtSWFpOW/N2MrsLQeOeWbT3lwA2jSp3lCwVmH+BPt5kV1YSq9WoTUXvIiIiBtREiMichwFJWV4WBa+XjZ+X7sXgHsT2vLw+R0cZR4c1p4NqTlM37if5TsznJKPcX9sIsjX07HK2IlYlsW0B89h5a4smgT51OzHiIiIuAkNJxMROYLdbpzO+42dwZB/zwZgV0YBlgX3D2l3zHNvXXMWvl4eTFqzj08XJvPkT2vJLSplcVI6Nw1sTVRw9TenjAjy5fzOUaf1HSIiIu5MPTEiIodkFZQwcNxM7o6P5dKe0RzILSa3uIzc4jK2peWSmlVIRJAP3p7H/v3H39uT1mEBfDQ/yXGtWYgfdgMdmzY6k58hIiLi9pTEiIgAGfklPPbDGgpKynl/zg5em7aFIztlvlycQmp2Ic2OmHh/tLioIDbty3Wcvzx5MwAx4ZXvDyMiIiKnRkmMiAjQ84VpjuOcorJj7i/YfpC92UUMalf13JZ7z2vHT6tSaRsRyJ3ntCE5PZ/G/t7ERQXVSswiIiINlZIYEWnQikrLGffHJsf51X1aMHFpilOZ+85ry9sztwFw8VnNq6yrbUQg3981gLAAH1qr90VERKTWaGK/iDRoMzam8cmCZMf5oyMP7/tyQbeKzSZvHNgaAMuCYZ0ij1tfr1ahSmBERERqmXpiRKTBSj6YT+LmNMf5yqeGEeLvzbhLu+Lt6cFFZzXnmdGdCA/04ZkLOzG4XRMXRisiIiJ/UhIjIg3ShHlJvPDbBsd502BfGgd4A3B135aO6xGNKpZGvvnsmDMboIiIiFRJSYyINDg5RaVOCcyYi7twcY+q57qIiIhI3aIkRkQalDlbDnDDR0sAGNQ2nOv6t+T8zlFYluXiyERERKS6lMSISIOxK73AkcAA/O+Wvtg8lLyIiIjUN1qdTEQajCnr9zmOpz94rhIYERGRekpJjIg0GCmZBY7j1mH+LoxEREREToeSGBFpEFIyCli7J9tx7mnTP38iIiL1lebEiIjbyysuY/DLsxzns/8R77pgRERE5LQpiRERt5RdWAoGAn09OZBb7Lg+tGMErcICXBiZiIiInC4lMSLiFjbty2HKuv3ERgQQFxXE0Nfm0LV5MD6eHizbmeko9/71vV0YpYiIiNQEJTEiUm+UldvJKixlwfZ0liSl8/miXXRvEUKPFiF8siD5mPJHzoFpHebPjIfi8dCKZCIiIvWekhgROWPmbDnAH+v28eIlXY7ZXLKwpJx9OUW0DvOvdONJuzG0feKPY657eliVJjBHeuHiLgxoE6ollUVERNyEkhgROWNu+3QZJWV27o6PpUXo4SWOp2/Yz22fLgPgqt4t+Nfl3Rz37HZDanYht0w5vDxyTHgAj46MY2jHSGweFqlZhbyTuI0bBrRm+OtzAJj3zwTK7QZfLxuRjXzP0BeKiIjImaAkRkRqVNLBfLxsFlGNfPG0eZBVUMKC7enEhAdQUmYHYNqG/VzcozlrdmfRv02YI4EBWLgj3XGcV1zGJ/OTeHXqFse1Nc8Op5Gvl9M7m4X4MebirhhjCA/04fbBMUQ31j4wIiIi7kpJjIictIz8EmweFsF+zsmEMYaEVxNP+Pzzv23g+d82OF2LiwqiabAvszYf4Lx/J/Ld3wbS84Vpjvu3d/XmiWuHHbdey7JY9uTQ6n+IiIiI1EvVSmIsy5oAdAImGWPGVHI/GJgI2IB84CpjTElNBioidcMvq1O5/6uVAPylezPuH9KOVmH+nP/GHHYcyD/uswNjwyizG5YkZRxz78Mbe+PnZaPXmOnsOJDPK1M2O+6Nv7Ynfumbj3lGREREGqYTJjGWZV0K2IwxAyzL+siyrHbGmK1HFbsWeM0YM82yrPHACOCXWohXRFzIGONIYKAioflldapTmX4xoXx5e388LMgpLOObZSmM6BKFt6cH4YE+7EzP5+P5yTxxQUfScor5bW0ql/aIJiq4Yt7Kxzf14eZPlvLVkl0AbBs7Ek+bB4mJSmJERESkQnV6YuKBbw4dTwUGAU5JjDHmnSNOmwBpNRGciNQt6fmHO1g/vrkP/1uQTOLmA/h6edC/TRgvXtKVJkE+jlXAgv29uP2cNk51tGkSyAsXdwGgZZg/d8e3dbqfEBfBgDZhLNyRzg0DWuFp86jlrxIREZH6xjLGHL9AxVCyt4wxqy3LGg70NMaMq6LsAGCMMWZIJffuAO4AiIyM7DVx4sTTDv5U5OXlERgY6JJ3S+1S29a++2bmk1tSMT/l7OZeJ37gFBlj2JhhJy7UA49Dyy2rfd2b2td9qW3dm9rXvbm6fRMSEpYbYyrdpbo6PTF5gN+h40Cg0j+LWpYVCrwNXFbZfWPM+8D7AL179zbx8fHVeHXNS0xMxFXvltqltq1d+7KLyJ08A+CEE+xrQsJR52pf96b2dV9qW/em9nVvdbl9qzNOYzkVQ8gAugPJRxewLMsb+BZ4zBizs8aiE5E6wW43XPifeQC8c21PF0cjIiIiDV11kpifgOsty3oNuBJYb1nW0SuU3Qr0BJ6wLCvRsqyrajhOEXGRcrvhzs+XcyC3mCt6RTOqa1NXhyQiIiIN3AmHkxljcizLigeGAS8bY/YBq48qMx4YXysRisgZl11YiocF/521nXdnb3dcf/rCTi6MSkRERKRCtfaJMcZkcniFMhFxY6tSsrjlk6Vk5Dtv9bT0iaEE+dbeZH4RERGR6qpWEiMi7m9Dag7P/bqexUdtRDn3kQRsHhZNgnxcFJmIiIiIMyUxInVUWbmdt2ZspbjMzqMj47AOLTV8sowxvDdnB+P+2MRNA1szY9N+mgT6MCA2jL4xYbSPDGR84nZ+XLGH3OIyerdqzPMXdWHS2lSah/jTItS/hr9MRERE5PQoiRGpo0a/PY9N+3IBuPnsGMeO9tVhjOGbZSn88/u1Ttc/WZAMQEpGISt2ZfHfWdud7n91e38GxIYB0KlZo9OIXkRERKT2KIkRqUOKSsvx8fRgwfZ0RwID0P+lGXjZLCY/cA4fz0+ia/NgLu0ZjddRu9kbY5i6YT8v/LaB3ZmFTveevKAjL/6+kdsHt2FU16bM3JTGmzO2AnBN35Y8PLw9YYEaMiYiIiJ1n5IYERczxvDmjK3Y7YZfVqeSnF7guOdt86Ck3A5AablhyL9nO+6Vlhuu69/KcZ6eV0yvMdMBiGrky9hLupDQIQJfLxuhAd4A3Da4jaN89xYh+HvbyCsu46HhHWr1G0VERERqkpIYERf7eVUqb0zfWum9t//ag34xoSzcns5dX6xwuvfkT+to7O/N2EkbuGVQDOv2ZAMQHujDL/edTUTQiYef3Xlu7Ol/gIiIiMgZpiRGxAW2H8hjf3YR7aOCeODrVU732kYEEuznxcG8Ys5t3wRfLxsjuzYl6aVRFJaWU2Y3/OXteSSnF3DPlxWJzZhJGwFoGuzLrIfj8fWynfFvEhERETlTlMSIVGHSmr20CvOnS/PgE5b9ZmkK0aF+DIwNr1bdRw4LAwjwtvHcRV0Y1Da8ygn8lmXh713xI5v4jwTu+WIFk9buBcDPy0ZhaTmvX3WWEhgRERFxe0piRI6wfGcG//huDT1bNua75bsBmPzAYOKiql6p68eVu3nk+zUALHpsiCMJWbErkz/W7uXxUR2dlkeev+2g43hQ23Byi0r59JZ+BPuf3EaST47uSNLBfAbGhvHEBR3ZnVmo5ZBFRESkQVASI3LIgdxiLhu/EIAdB/Id10e8MZfnL+pMsJ8XF53V/Jjn/u/r1Y7j5Tsz8bDg+xV7mL5xPwCtwgIY3ikSy7IY/PJMikorJupf2rM5r17eHQ+PU9v/pWmwH7//fbDjXAmMiIiINBRKYqTB2HEgj2s+WMQ9CW35a9+WeB6xPHF2YSl9xlas7DW4XTiFJeXck9CWVSlZvDljK0//vB6Av09cxYXdmzHmoi4E+3uRU1Tq9I4fVx5OXv705E/rePKndU7XOjdrxGtXnlUbnykiIiLi9pTESIPxxeJd7M8p5umf15NbVMZlPaMxGPZkFjomyANc2bsFF3ZvBsC57Zs49lL506+rU7FZ8MbVPfh2WcWQsx/uHsjr07Y4JTAx4QGcFxfBhHlJTs8P7xTJY6M61tZnioiIiLg9JTHi1lanZHH1+4toGuxLZkEJnZs1Yn1qDq9M2cwrUzY7le3fJpT3b+hNI9/Dc1M8PCw+ubkPJWV2BsSGkXywgFenbuanVaks25nJ3uwi2oQH0KNFCO9c25Onf15PsxBfHhja3rER5QND27FuTw7XfLCIK3pF88oV3c/ofwMRERERd6MkRtzWvyZvYnzidgB2HKyY43Jd/1bkF5c5bSgZ4G3jvI6RvH1Nj0rrie8Q4TjuGh3My5d3o9+LM9idWQjAO9f1xLIsgny9eP2qY4eIBfl6MSA2jMkPDKZNeGCNfZ+IiIhIQ+Vx4iIi9cu+7CJmbNzP+MTtRAT5MOHG3o57o7s1c5oMv/Cx81j//IgqE5jKRDbyZfY/4ukWHcxzf+l83JXLjhQX1QhvT/3IiYiIiJwu9cSI27AbwzM/r+N/C3c6rv12/yAignz55s4BzNyURoeoIAD+89ceJG4+QNNgv1N6V6uwAH65d1CNxC0iIiIiJ0dJjLiNmbvK+Hzj4QTm4eHtiQiq2LOlb0wofWNCHfdGd2vG6G7NzniMIiIiInL6lMSIW8gqKOHzjSUA/PH3wXRsWr0hXiIiIiJS/2iAvtR7uzMLOOv5aQD89689lcCIiIiIuDklMVLvvXNoBbLOYR6M7BLl4mhEREREpLZpOJnUa1v25zJxyS6u7deSYY3T8fCwXB2SiIiIiNQy9cRIvbTjQB4pGQW8OmUzgT6ePDy8g6tDEhEREZEzRD0xUu8s3J7ONR8scpxf3acFjQO8XRiRiIiIiJxJSmKkXii3Gz6en0SHqCCun7DE6d75mgcjIiIi0qAoiRGXKC2342Wr3mjGn1ft4e8TVzlde3BYe1IyCpi95QAJHSJqI0QRERERqaOUxMgZl5ZTxKB/zeLRkXHcMijmmPsb9+YQEx6Ar5ftmASmY9NGPDGqI4PahZ/JkEVERESkDlESI2fc1rQ8SsrtvDF9iyOJsdsN//fNKn5dnYrdwM1nt6ZLs2Ae+nY1AI+PiuOWs2PwrGbvjYiIiIi4LyUxUmsy80sI9vNyWvZ4Z3o+1364GICcojLmbDnAkz+t45q+Lfl5VSptIwLZlpbH1v157MksBKB7dDB3nBPrkm8QERERkbpHSYzUigXbDvLXDxdzT0Is7SODaBMeSNfoYL5akuJU7v/bu/cou+ry/uPvZ3JPhtwgTEi4GEQCMRiI4aJcMiPIpRRa8Ba1Ftql8Kt2oV38tOVWUVEo2ov0h1ZaUPEHIqUUa0HEIlHukIBAEBSQWwIJCQkJQ64z8/SPczIMJJDDZDLn7D3v11oszpz9nTlP8sk+s5/9/e59/vSyykX6f3fjowBcNGc/vnnz7/jZw0sAOHafnbj44zP7t3hJkiQ1NNfmqM9lJh+rzrZcfMsTfPaqX3Pc/7uNOx5fxr/88gkAfnH67E2+74Ap45k2aTQH7/Hq9S6tUyf0T9GSJEkqjJpmYiLiUmAacH1mnvcGY1qAazLz0D6sTwV0/7Mvbfb5jY3NV0+Yzu4Tmnnky0dz95Mv8q6dx7L05XXstv1IAI5+50S+8bPf8k9z9uV9e7X0W92SJEkqhi02MRFxIjAoM98TEZdFxDsy87HXjRkHfB8YtY3qVEGsXLOBE791BwA//NRBAOy361iumb+Qs69bAMDh1cZkxNBBtFZvjzy+x4dV7jh6OA+ee1R/li1JkqQCqWUmphW4uvr4JuAQ4LHXjekEPgL8uM8qU+F0dHYx55K7ur8+YMp4BlUv6j/sHRMYOqiJPSc2M3HM8HqVKEmSpBKIzHzzAZWlZBdl5gMRcSQwMzMveIOxczOz9Q22nQKcAtDS0vLuq666aqsK76329naam5vr8tpl98DSDv5x/joAvnbICCY1v/aSqw1dSQCDe9ytrC+ZbbmZb7mZb3mZbbmZb7nVO9+2xu65IgAAEKFJREFUtrb5mTlrc9tqmYlpB0ZUHzfTy5sBZOYlwCUAs2bNytbW1t78mK02d+5c6vXaZdbR2cVl358HLGWnMcP56LFtRGybZuWNmG25mW+5mW95mW25mW+5NXK+tTQx86ksIbsLmAH8dptWpEJZ9NIabntsKX/9Hw8B8KlDp3DWsdPqXJUkSZLKrJYm5jrg1oiYBBwDzImI8zLz7G1bmorg4At+8ZqvP3no7nWqRJIkSQPFFpuYzFwVEa3A+4ELM3Mx8MAbjG3t0+rU0G5csLj78fkn7kNTQMtoL9qXJEnStlXT58Rk5gpevUOZRPu6Dr74X5VbJl996ns4YMr4OlckSZKkgaKmJkZa1r6O6+5fxPuntdAUwaEX3gLAae/bwwZGkiRJ/comRlu0vqOLP7zoNhavWst51z/S/fyeLc382cFT6liZJEmSBqJe3S5ZA8uNDy9m8aq17NA8rPu51qkT+NnnDmPcqKF1rEySJEkDkU2MtujeJ5czbHATd53xPj707p3ZdfxILjtp/37/HBhJkiQJXE6mLfjvB5/jB3c9zUG7j2fwoCa+/qEZdHUlTU02MJIkSaoPZ2LUbe2GTp5dvppTLp/HD+58iq6u5C+vvB+ALxy9V/c4GxhJkiTVkzMxAip3H/vji29n4Yo1ANz0myX86rFlABwzfSIzdx1Xz/IkSZKkbjYxA9ySVWu57bFl/Oqxpd0NzEY//80S9tt1LBd/bGadqpMkSZI2ZRMzgHV0dnHg127u/nrMiCFc8ckDufa+RUwaO5zzrn+Ejx6wq8vHJEmS1FBsYgawH817tvvx4XvtyKcO253pk8cwffIYAI6ePpHJY0fUqzxJkiRps2xiBrBbf1e55mXsyCFcevL+m2zfedzI/i5JkiRJ2iLvTjaALW1fx+4TRnHL6a31LkWSJEmqmU3MALRwxWpWrt7AsvZ1TJ80hnGjhta7JEmSJKlmLicbQO7+/YssXLGGL/3kYVat7QDg8L1a6lyVJEmS9NbYxAwAF974KKvWbuD/3/XMJttm7ja2DhVJkiRJvWcTU1LrO7oYOriJX/5uKd+a+8Qm2+896wgykwnbDatDdZIkSVLv2cSUzA0PPc+nr7gPgCOntXDTb5Z0bxs9fDAXfnAGM3cba/MiSZKkwrKJKZGVazZ0NzBAdwNz7nHT+OiBu7J2QxdjRgypV3mSJElSn7CJKYknl71C2zfmAnDFJw9k0tgRLFi0kpbRwzlgyngAhg0eVMcKJUmSpL7hLZYLaP7Ty3nb31zP1fOe7X7urt+/CMCO2w3j4D12YMoOozhuxqTuBkaSJEkqC2diCiYz+cC37wTgC9c8yMxdxzJi6GBueOh5BjcFt//N++pcoSRJkrRt2cQ0qMzk01fcx4TthnHuce+kqSno7ErO+fGC14w76p9upbMrAThhv8kMGeTkmiRJksrNI94GtGZ9J3c88SI/XbCYy+98ms9ceR8vtq/j4edWcuXdlc96ufSkWQDdDQzA/5n99rrUK0mSJPUnZ2IayPMr1/DCqnWc/N17WLF6A+NHDeX4GZP43h1P8dMFizli7x27x87YZSx/sM9EbnhoMRd/bCbvffv2jBs1tI7VS5IkSf3DJqbOMpP9vvJz/mjGJL5/59Ov2Ta1ZTu+eNw0rrznGdZ3dPE/j7zAzuNGcPPpsxk2eBDf+vi761S1JEmSVD82Mf1s7YZOzrz2Ie5+cjmH770jl1cbl54NzJ8fPIW1HZ38xey3ExH89itH8+kr7mPhijX820mzvFWyJEmSBjSbmH72418v4tr7FwF0NzAbXfiBd/Hh/XfZ5Hsigm//ibMukiRJEnhh/zbVvq6DD//LnTy48CWgMgtz0c2Ps/2ooVx/2iHs0DyUM47ZiyfP/wMe/crRm21gJEmSJL2WMzF9rLMrufmRJSx6aQ3XzF/Iw8+t4qTL7uE7n5jFn/zb3azv7OLU2bvzzkljmHf2+7u/b/gQl4hJkiRJtbCJ6WNXz3uWM6596DXPrVi9gQ9/p/IBlWNGDOHUw7wVsiRJktRbLifrA5nJuo5OAG5/fNlrtn3+qKnsM3kMg5uCb87Zlwe+eCTjvRWyJEmS1Gs1zcRExKXANOD6zDyvt2Ma0aq1Gxg9fMhW/Yzzf/ooP7znGb56wj7c+9Ryjp8xiSGDmvjJg89x0nvfxmfa9uijaiVJkiRtsYmJiBOBQZn5noi4LCLekZmPvdUxjej5lWs44eI7OG7GTvzrrU8C0DZ1AkdMa+HjB+622e9Z1r6OF1atY9qk0SxYtJKTv3sPy9rXA3DaD+8HYP8p4/nEQbvx1ROme62LJEmS1MciM998QMRFwI2ZeUNEzAFGZOZ3ezHmFOAUgJaWlndfddVVffnnqFl7ezvNzc0AdHQl33lwHfcu7tzs2DMOGM6e45p4rj25Z3EH85d0sLC98vc1YjCs6aiM224I/Pk+w/jmfesYORi+Pnsko4ZEv/x59Kqe2ap8zLfczLe8zLbczLfc6p1vW1vb/MyctblttSwnGwUsqj5eDszszZjMvAS4BGDWrFnZ2tpaw0v3vblz59LztWfP7uLUH8xnzIghtE6dwIJFK7tnZc6/Z+0b/pyNDczZx+7N8TMmsePo4Zx07HpGDh3k7EudvD5blYv5lpv5lpfZlpv5llsj51tLE9MOjKg+bmbzNwOoZUxDGjKoictO3r/76z/adzKnHzmVnzzwHJ+/5sHu5z/T9naeeOEVTpm9O7uNH8lTL77C1ImjaR726l+hF+xLkiRJ214tTcx84BDgLmAG8NtejimM4UMG8aFZu3DE3i28+Mo69thxu03GbN88rA6VSZIkSaqlibkOuDUiJgHHAHMi4rzMPPtNxhzU96X2v3GjhjLO2RVJkiSpoWxx2VdmrgJaqcyytGXmA69rYDY3ZmXflypJkiRJNX5OTGauAK7e2jGSJEmStLUKcwG+JEmSJIFNjCRJkqSCsYmRJEmSVCg2MZIkSZIKxSZGkiRJUqHYxEiSJEkqFJsYSZIkSYViEyNJkiSpUGxiJEmSJBVKZGb/v2jEUuDpfn/hih2AZXV6bW1bZltu5ltu5lteZltu5ltu9c53t8ycsLkNdWli6iki5mXmrHrXob5ntuVmvuVmvuVltuVmvuXWyPm6nEySJElSodjESJIkSSqUgdjEXFLvArTNmG25mW+5mW95mW25mW+5NWy+A+6aGEmSJEnFNhBnYiRJkiQVmE2MJEmSpEIpdRMTEaX+8w10ETGo3jVI6h3fn8srIqLeNWjbcd8tr6IdV5XuH2JEHB4RZ0bE9pnZVe961HeiYkJEfBcgMzvrXZP6VkTsExENeT96bb2IeH9EfDIiRvj+XC4R0RYRxwKkF9uWjsdW5VT046pSNTER8XXg00AL8FcRMbnOJakPVX8xNgHHRMQHwTNCJfQZYFy9i1Dfi4gLgTOAmcDp1ec8Y19gG/OLiL8EzgSOiIjPRsSe9a1MfaFHvt/AY6tS2Zht0Y+rClNojZYDnwK+ALwT8BdkSfTYqSYCzwMnR8TIzOwq2vSnNhURQ6oPnwGOiojjI+LgiBhT3e6+XHwvUHl//iyVg90hnrEvrogYxqsnHEYAP8jMv6KS83ERMb5uxWmrvS7fpcApeGxVCtVsx/R4ahIFPa4qdBMTEdMj4mvVxyOBecCqzFxHpaHxbFCB9cy3hw3AxcBdVA6GCjf9qYqe+WbmhurTbcAr1ccfAP66ut2D3YLZzPszwKnAHOB3wCciorVO5WkrRMSfAf8NfD0i2oAOoKl6sul2oBM4qo4laiv0yPeCiDgSuB94yWOr4uuR7d9HxGHVE4RrgW9RwOOqQjcxwB7AxyNiemauzsyfZ2ZHRIwC9szMX8BrzvKqWDbmO63HGtydgLdl5nnACRHxo4jYuX4lait059vjufnAssz8L+AbwGSXLhTWxnz3yczVwOXA3cDbgAuAscCR1fdrFURETACOB/4CWADsDTwF7AdsDywCngYm9WheVRCvy/cRYL/MvCkzOz22KrbXZfsgcEj1BOEuwG5FPK4qVBMTEbtGxEciYqfqUyOB/wTO6jFmCJWzQndGxNSIOA84qP+r1Vv1Jvme02NYJ7A+Iv4BaKbS0Czs51LVCzXmu5pKvk3AzlQ+kHdRP5eqXtjS+3NmvkBlecovMvP3VM7+Tc7MVzb7A9UwemQ7kcp+eXVmPg78EvhgZv4nlTyPBQYBK4FDq82rGtyb5HsLlUw3LunuAO7w2Ko4trDvHlMdthrYEBF/T8GOqwrTxETEdOAa4F3AWRFxAPDjzPwclWnsD0H3spQ9gf8LfAd4ITNvrVPZqlEN+X64OnQwcAKwIDOnAV+ufr9rdBtYDfl+pDr0BmAH4HvAhVSWiKrBbSHf6LH/LgE+FxFXAX9HZemRGliPbGdQ+b26XWb+sLp5OfBk9fFlwHZUlqocBzzcz6WqF7aQ70vAowDV1RB7Ap/HY6tCqDVbKr3AHwMPF+24anC9C9iSiPgAlYOaXwGPZuZZEXEUcDCVM31zqSw7+VJE/Ed1RxsHfAn458xcXp/KVYu3kO+XI+KazPyfiDgsM18GyMzrq//3mokG9Bb333/PzHkRMR84hEqjuqJOpasGvdh/fxIRj1A5g3tadXZGDWgz2Z5ZzfaAiOjIzDuAyVRmXQCGAP9KZTnZy8Cv61C2avQW8n25Ov5dVJYdnQN822OrxvVWswUWA8dm5nNQrOOqhm1iImIwcGn1yzXA7sDiiBgH3AaMBt4TEfMy896IeJJK43IOcHtm/qoedas2vcj398C5wN9m5ssR0eS96htXL/ffc6nkm4Bn+BpYL/ffLwHnVJcyPF6PurVlNWY7OyLupnIdzPjqzNoLwJmZeW0dylaNepHv9hHxIyrXOV2w8QBXjWcr9t0lwNlFPK5q5OVkXcATmXkSlamtQ6is59ujuob6UWAolYtEodK83ALFuavCANebfOdu/Oai7WgD0Fblq4bX6/dnNbxash1J5UxuC/Be4MrMPC0z2+tUs2rXm3yvyMwvOPvS8Hq77342M18u4nFVIzcxTVQuPKI6xfUb4DngDyNiDyphtFHpNsnM5RvvmKFCMN9yM99yM9/yqiXbQ6tjv5eZe1bvJqhiMN/yGnDZNuxysszsoBpGROwCTMnMIyJiDvAVYAWVtbee+Skg8y038y038y2vGrN9Hlibmc/Ur1L1hvmW10DMtmGbmI2qt/XbQOW2fnsDU4GfUQnj2cxcUs/6tHXMt9zMt9zMt7xqyNabMhSY+ZbXQMo2CnDzASLieOA64CYq6/cur3NJ6kPmW27mW27mW15mW27mW14DJduiNDFtwIHAP2Tm+nrXo75lvuVmvuVmvuVltuVmvuU1ULItShMTRbhftXrHfMvNfMvNfMvLbMvNfMtroGRbiCZGkiRJkjZq5FssS5IkSdImbGIkSZIkFYpNjCSp7iJi34jYt951SJKKwSZGktQI9q3+J0nSFnlhvySpriLifOCE6peLMvPwetYjSWp8NjGSpLqLiJMBMvN79a1EklQELieTJEmSVCg2MZKkRrAGGAmVD2qrcy2SpAZnEyNJagQ/B06MiNuBQ+tdjCSpsXlNjCRJkqRCcSZGkiRJUqHYxEiSJEkqFJsYSZIkSYViEyNJkiSpUGxiJEmSJBWKTYwkSZKkQrGJkSRJklQo/wvYoqSHh0SBSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2['滚动找RP']['2010-01-01':].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b8bcf122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1acad9ffdc0>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2['滚动找RP'].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "bf4761f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aca73ace20>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dddd2['滚动找RP'].cumsum().plot(figsize=(14,6),grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523a3b44",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b4d72399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t\n",
       "2010-12-31    0.000000\n",
       "2011-12-31    0.000000\n",
       "2021-12-31    0.008781\n",
       "2014-12-31    0.032389\n",
       "2018-12-31    0.036836\n",
       "2015-12-31    0.039969\n",
       "2012-12-31    0.040883\n",
       "2019-12-31    0.047138\n",
       "2023-12-31    0.048679\n",
       "2022-12-31    0.059332\n",
       "2016-12-31    0.059678\n",
       "2017-12-31    0.071134\n",
       "2013-12-31    0.096832\n",
       "2020-12-31    0.126810\n",
       "Name: 滚动找RP, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddd2['滚动找RP'].resample('Y').sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46492765",
   "metadata": {},
   "outputs": [],
   "source": [
    "dddd2['滚动找RP'].resample('Y').sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3644cba5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a51b6255",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c21b4c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>主次差因子</th>\n",
       "      <th>展期因子-三月</th>\n",
       "      <th>展期因子-一月</th>\n",
       "      <th>展期因子-二月</th>\n",
       "      <th>开盘因子-三月</th>\n",
       "      <th>开盘因子-一月</th>\n",
       "      <th>开盘因子-二月</th>\n",
       "      <th>开盘因子2-三月</th>\n",
       "      <th>开盘因子2-一月</th>\n",
       "      <th>开盘因子2-二月</th>\n",
       "      <th>...</th>\n",
       "      <th>价格因子</th>\n",
       "      <th>基差变化因子</th>\n",
       "      <th>展期变化因子</th>\n",
       "      <th>curveRSI因子</th>\n",
       "      <th>展期因子合并</th>\n",
       "      <th>开盘因子-三月合并</th>\n",
       "      <th>开盘因子-一月合并</th>\n",
       "      <th>开盘因子合并</th>\n",
       "      <th>滚动找最优</th>\n",
       "      <th>滚动找RP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>0.001177</td>\n",
       "      <td>-0.002172</td>\n",
       "      <td>0.004565</td>\n",
       "      <td>0.002816</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.004565</td>\n",
       "      <td>0.004522</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>-0.000463</td>\n",
       "      <td>-0.001542</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.004565</td>\n",
       "      <td>0.003384</td>\n",
       "      <td>0.003975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.004121</td>\n",
       "      <td>0.001321</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001455</td>\n",
       "      <td>-0.003207</td>\n",
       "      <td>-0.004764</td>\n",
       "      <td>0.000776</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>0.001441</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>-0.004618</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.003820</td>\n",
       "      <td>-0.005483</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.002238</td>\n",
       "      <td>-0.001625</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001122</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>-0.000114</td>\n",
       "      <td>-0.005729</td>\n",
       "      <td>0.001051</td>\n",
       "      <td>0.001426</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.002359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.002128</td>\n",
       "      <td>-0.002128</td>\n",
       "      <td>-0.000235</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>0.003758</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>0.000674</td>\n",
       "      <td>0.005007</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>-0.004372</td>\n",
       "      <td>-0.004249</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>-0.001497</td>\n",
       "      <td>0.003162</td>\n",
       "      <td>0.002730</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002870</td>\n",
       "      <td>0.002870</td>\n",
       "      <td>-0.000752</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.001868</td>\n",
       "      <td>-0.001795</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>-0.000405</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>-0.003569</td>\n",
       "      <td>-0.001945</td>\n",
       "      <td>-0.001117</td>\n",
       "      <td>0.001663</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.001644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-11</th>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.001243</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>-0.001561</td>\n",
       "      <td>0.000607</td>\n",
       "      <td>-0.001665</td>\n",
       "      <td>-0.002403</td>\n",
       "      <td>-0.000344</td>\n",
       "      <td>-0.002113</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>-0.001440</td>\n",
       "      <td>-0.001647</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>-0.001842</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-0.000776</td>\n",
       "      <td>0.001740</td>\n",
       "      <td>0.001562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-12</th>\n",
       "      <td>-0.002150</td>\n",
       "      <td>0.002112</td>\n",
       "      <td>0.002275</td>\n",
       "      <td>0.002373</td>\n",
       "      <td>-0.001243</td>\n",
       "      <td>0.003437</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>-0.000982</td>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.001957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000544</td>\n",
       "      <td>0.003456</td>\n",
       "      <td>-0.003072</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.002253</td>\n",
       "      <td>-0.001156</td>\n",
       "      <td>0.002825</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.000229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-13</th>\n",
       "      <td>0.003780</td>\n",
       "      <td>-0.003843</td>\n",
       "      <td>-0.006460</td>\n",
       "      <td>-0.006967</td>\n",
       "      <td>0.013664</td>\n",
       "      <td>0.009909</td>\n",
       "      <td>0.011893</td>\n",
       "      <td>0.011599</td>\n",
       "      <td>0.007866</td>\n",
       "      <td>0.012222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006734</td>\n",
       "      <td>-0.001891</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>-0.006566</td>\n",
       "      <td>-0.005757</td>\n",
       "      <td>0.012975</td>\n",
       "      <td>0.009228</td>\n",
       "      <td>0.011102</td>\n",
       "      <td>-0.001875</td>\n",
       "      <td>-0.001272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-14</th>\n",
       "      <td>0.005768</td>\n",
       "      <td>-0.006663</td>\n",
       "      <td>-0.003768</td>\n",
       "      <td>-0.005199</td>\n",
       "      <td>0.002307</td>\n",
       "      <td>-0.001388</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.004072</td>\n",
       "      <td>-0.001289</td>\n",
       "      <td>-0.000080</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003006</td>\n",
       "      <td>-0.005435</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>-0.004356</td>\n",
       "      <td>-0.005210</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>-0.001355</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>-0.001552</td>\n",
       "      <td>-0.001253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-15</th>\n",
       "      <td>0.001460</td>\n",
       "      <td>-0.001718</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>-0.000991</td>\n",
       "      <td>-0.004231</td>\n",
       "      <td>-0.003267</td>\n",
       "      <td>-0.004817</td>\n",
       "      <td>-0.002785</td>\n",
       "      <td>-0.002643</td>\n",
       "      <td>-0.004509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>-0.000785</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>-0.000620</td>\n",
       "      <td>-0.000769</td>\n",
       "      <td>-0.003749</td>\n",
       "      <td>-0.003059</td>\n",
       "      <td>-0.003404</td>\n",
       "      <td>-0.000146</td>\n",
       "      <td>-0.000023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3251 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               主次差因子   展期因子-三月   展期因子-一月   展期因子-二月   开盘因子-三月   开盘因子-一月  \\\n",
       "t                                                                        \n",
       "2010-08-02       NaN  0.001177  0.001177 -0.002172  0.004565  0.002816   \n",
       "2010-08-03       NaN  0.001730  0.001730  0.000640  0.001441  0.004121   \n",
       "2010-08-04       NaN  0.003886  0.003886 -0.004618  0.001426  0.003820   \n",
       "2010-08-05       NaN -0.002128 -0.002128 -0.000235  0.003162  0.003758   \n",
       "2010-08-06       NaN  0.002870  0.002870 -0.000752  0.002178  0.001868   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2023-12-11  0.000948  0.000510  0.001243  0.000799 -0.001561  0.000607   \n",
       "2023-12-12 -0.002150  0.002112  0.002275  0.002373 -0.001243  0.003437   \n",
       "2023-12-13  0.003780 -0.003843 -0.006460 -0.006967  0.013664  0.009909   \n",
       "2023-12-14  0.005768 -0.006663 -0.003768 -0.005199  0.002307 -0.001388   \n",
       "2023-12-15  0.001460 -0.001718  0.000402 -0.000991 -0.004231 -0.003267   \n",
       "\n",
       "             开盘因子-二月  开盘因子2-三月  开盘因子2-一月  开盘因子2-二月  ...      价格因子    基差变化因子  \\\n",
       "t                                                   ...                       \n",
       "2010-08-02  0.001286  0.004565  0.004522  0.002933  ...  0.000358 -0.004509   \n",
       "2010-08-03  0.001321  0.001441  0.001081  0.001244  ... -0.001455 -0.003207   \n",
       "2010-08-04 -0.005483  0.001426  0.002238 -0.001625  ... -0.001122  0.000199   \n",
       "2010-08-05  0.006764  0.003162  0.000674  0.005007  ...  0.000882 -0.004372   \n",
       "2010-08-06 -0.001795  0.002178 -0.000405  0.001643  ...  0.000544 -0.003569   \n",
       "...              ...       ...       ...       ...  ...       ...       ...   \n",
       "2023-12-11 -0.001665 -0.002403 -0.000344 -0.002113  ... -0.000083 -0.001440   \n",
       "2023-12-12  0.000271 -0.000982  0.001601  0.001957  ...  0.000544  0.003456   \n",
       "2023-12-13  0.011893  0.011599  0.007866  0.012222  ... -0.006734 -0.001891   \n",
       "2023-12-14  0.000704  0.004072 -0.001289 -0.000080  ... -0.003006 -0.005435   \n",
       "2023-12-15 -0.004817 -0.002785 -0.002643 -0.004509  ...  0.000052 -0.000785   \n",
       "\n",
       "              展期变化因子  curveRSI因子    展期因子合并  开盘因子-三月合并  开盘因子-一月合并    开盘因子合并  \\\n",
       "t                                                                            \n",
       "2010-08-02 -0.000463   -0.001542  0.000061   0.004565   0.003384  0.003975   \n",
       "2010-08-03 -0.004764    0.000776  0.001367   0.001441   0.003107  0.002274   \n",
       "2010-08-04 -0.000114   -0.005729  0.001051   0.001426   0.003293  0.002359   \n",
       "2010-08-05 -0.004249    0.002367 -0.001497   0.003162   0.002730  0.002946   \n",
       "2010-08-06 -0.001945   -0.001117  0.001663   0.002178   0.001111  0.001644   \n",
       "...              ...         ...       ...        ...        ...       ...   \n",
       "2023-12-11 -0.001647    0.002227  0.000851  -0.001842   0.000290 -0.000776   \n",
       "2023-12-12 -0.003072    0.002143  0.002253  -0.001156   0.002825  0.000834   \n",
       "2023-12-13  0.000096   -0.006566 -0.005757   0.012975   0.009228  0.011102   \n",
       "2023-12-14  0.003693   -0.004356 -0.005210   0.002895  -0.001355  0.000770   \n",
       "2023-12-15  0.000770   -0.000620 -0.000769  -0.003749  -0.003059 -0.003404   \n",
       "\n",
       "               滚动找最优     滚动找RP  \n",
       "t                               \n",
       "2010-08-02       NaN       NaN  \n",
       "2010-08-03       NaN       NaN  \n",
       "2010-08-04       NaN       NaN  \n",
       "2010-08-05       NaN       NaN  \n",
       "2010-08-06       NaN       NaN  \n",
       "...              ...       ...  \n",
       "2023-12-11  0.001740  0.001562  \n",
       "2023-12-12  0.000846  0.000229  \n",
       "2023-12-13 -0.001875 -0.001272  \n",
       "2023-12-14 -0.001552 -0.001253  \n",
       "2023-12-15 -0.000146 -0.000023  \n",
       "\n",
       "[3251 rows x 32 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dddd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aa092e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6adf74d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "483e0abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8428519091542521"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*dddd2['滚动找RP'].mean()/dddd2['滚动找RP'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "9d7ae3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.854875348437001"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*dddd2['滚动找RP'].mean()/dddd2['滚动找RP'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "f49f5f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8512166935970866"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*dddd2['滚动找RP'].mean()/dddd2['滚动找RP'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "7735fd2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8379833208751453"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(250**0.5)*dddd2['滚动找RP'].mean()/dddd2['滚动找RP'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ea780fd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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